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The thesis is that LatAm agriculture is digitizing a decade behind its US/EU peers, the incumbent is an unmaintained on-prem product, and a founder with genuine domain roots can win the category co-op by co-op.","origin":"agent_drafted","sources":[{"source_id":"claim_arr","source_type":"claim"},{"source_id":"claim_growth","source_type":"claim"},{"source_id":"finding_competitor","source_type":"finding"}],"confidence":"high","section_id":"executive_summary","contains_projection":false,"contains_contradiction":false,"contains_unverified_claim":true},{"id":"p_exec_2","order":1,"prose":"Two issues must be resolved before a commitment. First, customer concentration is high: the top 3 co-ops represent 62% of ARR, so the loss of a single anchor would materially impair the growth story — the partner should understand contract length and switching costs for those three. Second, the data room is incomplete: there is no financial model (so unit economics and runway are unverified) and no cap table (so existing dilution is unknown). A secondary concern is FX — ARR is billed in MXN/COP but reported in USD unhedged, making the headline figure sensitive to currency moves.","origin":"agent_drafted","sources":[{"source_id":"claim_concentration","source_type":"claim"},{"source_id":"financial_model","source_type":"gap"}],"confidence":"high","section_id":"executive_summary","contains_projection":false,"contains_contradiction":false,"contains_unverified_claim":true},{"id":"p_recommendation_placeholder","order":0,"prose":"[Partner to complete]","origin":"partner_only_placeholder","sources":[],"confidence":"n/a","section_id":"recommendation","contains_projection":false,"contains_contradiction":false,"contains_unverified_claim":false},{"id":"p_company_1","order":0,"prose":"Lattice sells a smartphone-based SaaS to agricultural co-operatives, which are member-owned organizations that aggregate smallholder farmers to buy inputs and sell crops collectively. The product digitizes member registries, harvest intake and weigh-ins, per-member payment ledgers, and input distribution — workflows that co-ops run today on paper and Excel or on Cropsifter, an on-premise desktop tool from the pre-smartphone era. Current footprint is 14 paying co-ops across Mexico and Colombia, ranging from ~200 to ~4,000 members each.","origin":"agent_drafted","sources":[{"source_id":"claim_coops","source_type":"claim"}],"confidence":"high","section_id":"company_overview","contains_projection":false,"contains_contradiction":false,"contains_unverified_claim":false},{"id":"p_market_1","order":0,"prose":"There are tens of thousands of agricultural co-ops across Latin America managing a large share of the region's coffee, cacao, and produce output. The incumbent software (Cropsifter) is on-premise, predates mobile, and has roughly 150 co-op customers — a proof that co-ops will pay for software, and that the incumbent is beatable on form factor. Digitization tailwinds (smartphone penetration, buyer traceability requirements, and government formalization programs) are pulling co-ops toward modern tooling. The near-term obtainable market is smaller than the headline given fragmentation and long, relationship-driven sales cycles, but the category has a defensible wedge for a domain-native founder.","origin":"agent_drafted","sources":[{"source_id":"finding_competitor","source_type":"finding"}],"confidence":"medium","section_id":"market","contains_projection":false,"contains_contradiction":false,"contains_unverified_claim":false},{"id":"p_team_1_factual","order":0,"prose":"Two-person founding team. Marisol Vega (CEO) spent four years in McKinsey's Bogotá office in the agribusiness practice (2018–2022, verified via alumni directory and a former manager) and holds a Wharton MBA (2020); she grew up on a coffee farm in Antioquia that her family still operates. Tomás Aguirre (CTO) was an engineer at Globant before co-founding Lattice. The pairing is a domain-and-go-to-market lead with a hands-on technical co-founder.","origin":"agent_drafted","sources":[{"source_id":"finding_marisol_mck","source_type":"finding"},{"source_id":"bg_001","source_type":"qa_answer"}],"confidence":"high","section_id":"team","contains_projection":false,"contains_contradiction":false,"contains_unverified_claim":false},{"id":"p_team_2_prior_work","order":1,"prose":"Founder-market fit is unusually direct: Marisol's family co-op was Lattice's first pilot, and reference calls with two co-op managers described her as a genuine insider rather than a software vendor. Her McKinsey agribusiness work gives her a portfolio view of how dozens of co-ops operate, and the domain relationships shorten what is otherwise a trust-heavy, relationship-driven sales cycle. This is the single strongest element of the deal.","origin":"agent_drafted","sources":[{"source_id":"fmf_001","source_type":"qa_answer"},{"source_id":"bg_001","source_type":"qa_answer"}],"confidence":"high","section_id":"team","contains_projection":false,"contains_contradiction":false,"contains_unverified_claim":false},{"id":"p_team_3_partner","order":2,"prose":"[Partner to complete — character assessment, founder-market fit judgment, and overall team score]","origin":"partner_only_placeholder","sources":[],"confidence":"n/a","section_id":"team","contains_projection":false,"contains_contradiction":false,"contains_unverified_claim":false},{"id":"p_traction_1","order":0,"prose":"Company-stated traction is $480K ARR across 14 co-ops with 18% month-over-month growth. The growth rate is strong for a relationship-driven sales motion, but the base is small and the mix is concentrated — the top 3 co-ops account for 62% of ARR per the customer list. Two co-op managers confirmed live, daily use of the product in reference calls, which is a positive durability signal. The ARR figure is reported in USD but billed in local currency, so treat it as approximate pending the local-currency book.","origin":"agent_drafted","sources":[{"source_id":"claim_arr","source_type":"claim"},{"source_id":"claim_concentration","source_type":"claim"}],"confidence":"medium","section_id":"traction","contains_projection":false,"contains_contradiction":false,"contains_unverified_claim":true},{"id":"p_business_model_1","order":0,"prose":"Lattice charges co-ops a per-member annual subscription with tiered pricing by co-op size. Unit economics cannot be evaluated because no financial model was provided — this is the most important gap in the data room. Additionally, the company bills in MXN/COP but reports ARR in USD without hedging and re-prices only annually, so a 10–15% currency move would swing reported revenue materially. Partner should request the financial model, the local-currency ARR book, and the annual re-pricing history.","origin":"agent_drafted","sources":[{"source_id":"fx_001","source_type":"qa_answer"},{"source_id":"financial_model","source_type":"gap"}],"confidence":"medium","section_id":"business_model","contains_projection":false,"contains_contradiction":false,"contains_unverified_claim":true},{"id":"p_competition_1","order":0,"prose":"The dominant incumbent is Cropsifter, a legacy on-premise co-op management tool with ~150 customers that predates the smartphone era; Lattice positions as its mobile-first replacement. A smaller adjacent player, AgriTech Pro, focuses on single-farm management rather than co-ops. Lattice's defensibility rests on being embedded in the co-op's daily payment and intake workflows plus the founder's relationship moat, rather than on any structural network effect — a real but not impregnable position at this stage.","origin":"agent_drafted","sources":[{"source_id":"finding_competitor","source_type":"finding"}],"confidence":"medium","section_id":"competition_moat","contains_projection":false,"contains_contradiction":false,"contains_unverified_claim":false},{"id":"p_outcomes_1","order":0,"prose":"Base case: Lattice becomes the default co-op operating system in its two beachhead countries, expands across LatAm, and reaches a mid-eight-figure revenue business supporting a strategic acquisition by an agribusiness input supplier or a regional software roll-up. At the $12M entry and a $1M check for ~8%, a credible regional-leader outcome returns several multiples over a 6–8 year horizon. Downside: concentration bites (an anchor co-op churns), FX erodes reported growth, and the company stalls as a sub-scale regional tool — at which point the position is a partial loss or write-off. The partner judgment fields below capture the actual underwriting call.","origin":"agent_drafted","sources":[{"source_id":"claim_concentration","source_type":"claim"},{"source_id":"finding_competitor","source_type":"finding"}],"confidence":"medium","section_id":"outcomes_analysis","contains_projection":true,"contains_contradiction":false,"contains_unverified_claim":false},{"id":"p_outcomes_2_partner","order":1,"prose":"[Partner to complete — base-case target multiple, upside thesis, downside floor, target ownership]","origin":"partner_only_placeholder","sources":[],"confidence":"n/a","section_id":"outcomes_analysis","contains_projection":false,"contains_contradiction":false,"contains_unverified_claim":false},{"id":"p_risks_1","order":0,"prose":"Three items before commitment. First, concentration: obtain contract terms, tenure, and switching costs for the top-3 co-ops (62% of ARR) and understand what happens to the plan if one leaves. Second, the missing financial model and cap table: request both — the model to underwrite unit economics and runway, the cap table to understand existing dilution before pricing the round. Third, FX: get the local-currency ARR book and understand the re-pricing cadence. 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/api/diligence/7c63c037-60e6-4363-afce-543077bfe5fa/drafts":{"status":200,"body":[{"id":"f1f60149-7291-4722-923c-bb06745d8403","draft_version":"v0.3-final","agent_version":"memo-agent v0.1","is_draft":true,"finalized_at":null,"created_at":"2026-07-01T15:00:19.584867+00:00","ingestion_output":{"documents":[{"claims":[{"id":"claim_arr_q4","field":"ARR_q4_2025","value":"$1.2M","context":"slide 4, traction","criticality":"high","verification_status":"unverified"},{"id":"claim_growth","field":"MoM_growth_q4","value":"25%","context":"slide 4, traction","criticality":"high","verification_status":"unverified"},{"id":"claim_nrr","field":"NRR_q4_2025","value":"138%","context":"slide 5, retention","criticality":"high","verification_status":"unverified"},{"id":"claim_gm","field":"gross_margin","value":"81%","context":"slide 8, unit economics","criticality":"medium","verification_status":"unverified"},{"id":"claim_burn","field":"burn_monthly","value":"$400K/mo","context":"slide 9, 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Highlights $1.2M ARR, 25% MoM growth, 47 paying customers including Notion and Linear. Founders are ex-Vercel and ex-Cloudflare.","document_id":"stellate_deck","detected_type":"pitch_deck","type_confidence":"high"},{"claims":[{"id":"claim_y3_arr","field":"ARR_2027_projected","value":"$18M","context":"forecast tab, row 14","criticality":"high","verification_status":"unverified"},{"id":"claim_y3_burn","field":"burn_2027_avg","value":"$650K/mo","context":"P&L tab, monthly avg","criticality":"medium","verification_status":"unverified"}],"summary":"Three-year financial model showing path from $1.2M ARR to $18M ARR by end of 2027. Aggressive net expansion assumption (140% NRR sustained at $5M+ ARR scale).","document_id":"stellate_model","detected_type":"financial_model","type_confidence":"high"},{"claims":[{"id":"claim_founder_ownership","field":"founder_ownership_pre_a","value":"64%","context":"cap table summary","criticality":"medium","verification_status":"unverified"},{"id":"claim_option_pool","field":"option_pool_post_a","value":"11%","context":"cap table summary","criticality":"low","verification_status":"unverified"}],"summary":"Pre-Series A cap table. Founders own 64% pre-money. Existing investors: Bain Capital Ventures (seed lead), Initialized Capital, and 3 angels. Option pool is 11% post-Series A.","document_id":"stellate_captable","detected_type":"cap_table","type_confidence":"high"},{"claims":[],"summary":"Curated customer references list with three named contacts at Notion, Linear, and Ramp.","document_id":"stellate_refs","detected_type":"customer_references","type_confidence":"high"},{"claims":[{"id":"claim_ceo_role","field":"ceo_prior_company","value":"Vercel","context":"bio page 1","criticality":"medium","verification_status":"unverified"},{"id":"claim_cto_role","field":"cto_prior_company","value":"Cloudflare","context":"bio page 2","criticality":"medium","verification_status":"unverified"}],"summary":"Founder bios for Alex Chen (CEO, ex-Vercel), Priya Mehta (CTO, ex-Cloudflare), Devon Rhodes (VP Eng, ex-Vercel).","document_id":"stellate_team","detected_type":"team_bio","type_confidence":"medium"}],"gap_analysis":{"missing":[{"rationale":"Edge-caching tool sees customer database queries. 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Implied 60% burn growth — partner should ask about hiring plan."}]},"research_output":{"findings":[{"id":"finding_arr_logos","topic":"Customer logos verification","sources":[{"url":null,"tier":"tier_1","title":"Reference call notes — Notion VP Eng"},{"url":"https://linear.app/blog/caching-at-scale","tier":"tier_2","title":"Linear engineering blog: caching at scale"},{"url":"https://stellate.dev/customers","tier":"tier_3","title":"Stellate customer page"}],"evidence":"Notion confirmed via reference call with their VP Engineering on Apr 30. Linear engineering blog post mentions Stellate by name (Aug 2025). Ramp listed as customer on Stellate marketing site.","claim_ref":"claim_customers","verification_status":"verified"},{"id":"finding_nrr","topic":"NRR sustainability at scale","sources":[{"url":null,"tier":"tier_2","title":"PlanetScale Series C announcement (Bessemer)"}],"evidence":"Could not externally verify the 138% NRR claim. Comparable infra companies (PlanetScale, Upstash) report NRR in 120-130% range at similar scale. 138% is plausible but at the high end.","claim_ref":"claim_nrr","verification_status":"inconclusive"},{"id":"finding_team","topic":"Founder credentials","sources":[{"url":null,"tier":"tier_1","title":"Vercel team page (Wayback, Aug 2023)"},{"url":null,"tier":"tier_1","title":"Cloudflare GitHub org"}],"evidence":"Alex Chen profile on Vercel's engineering team page (archived 2023). Co-author of Vercel's Edge Network blog post (2022). Priya Mehta listed as Cloudflare staff engineer in three GitHub repos. All three roles match deck claims.","claim_ref":"claim_ceo_role","verification_status":"verified"}],"research_gaps":[{"topic":"Customer concentration in top 10","rationale":"Deck does not disclose what % of ARR comes from the top 10 customers. If concentration is >40% the growth claim is more fragile.","criticality":"important"},{"topic":"Churn cohort behavior","rationale":"NRR alone does not show gross retention. Need to see Day-365 logo retention.","criticality":"important"},{"topic":"Why existing investors aren't taking the round","rationale":"Bain Capital Ventures (seed lead) is pro-rata-eligible. Their non-participation could be informative.","criticality":"should_address"}],"research_mode":"no_web_search","contradictions":[{"topic":"Burn rate growth","severity":"material","claim_ref":"claim_burn","description":"Deck states $400K/mo burn at $1.2M ARR. Model implies $650K/mo average burn over the next two years. Either burn grows 60% or revenue grows much faster than the company is letting on. 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Newer entrant."}]},"founder_dossiers":[{"role":"CEO & Co-founder","sources":[{"url":null,"title":"Vercel team page (Wayback, Aug 2023)"},{"url":null,"title":"GitHub commit history — vercel/edge-cache repo"}],"founder_name":"Alex Chen","open_questions":["What was the specific scope of his work at Vercel — did he own the cache invalidation system or contribute to it?","Why leave Vercel? Was the founding decision triggered by a specific customer pain point or general entrepreneurial ambition?"],"background_summary":"Spent 4 years at Vercel as a senior engineer on the Edge Network team. Co-authored the Vercel Edge Network blog post that announced their cache invalidation system. Stanford CS '18."}]},"qa_answers":[{"category":"background_track_record","partner_id":null,"answer_text":"References were uniformly positive on Alex specifically. The strongest signal: Notion's VP Eng said \"we evaluated five caching solutions and Stellate was the only one that didn't require us to refactor our database access layer.\" That is the kind of integration ergonomics that becomes a moat.","answered_at":"2026-06-28T15:00:12.638Z","question_id":"bg_001","feeds_dimensions":["team"]},{"category":"background_track_record","partner_id":null,"answer_text":"Verified Alex's Vercel role via Wayback and a former colleague. Priya at Cloudflare confirmed via her old manager. We did not verify Stanford CS — not material.","answered_at":"2026-06-28T15:00:12.639Z","question_id":"bg_002","feeds_dimensions":["team"]},{"category":"founder_market_fit","partner_id":null,"answer_text":"Yes — surface answer was \"we hated database bills at Vercel\" but the deeper answer that emerged on call 3 was that Alex's last project at Vercel was an internal tool for cache invalidation that the company decided not to productize. Founder-product fit is unusually tight here.","answered_at":"2026-06-28T15:00:12.639Z","question_id":"fmf_001","feeds_dimensions":["team"]},{"category":"founder_market_fit","partner_id":null,"answer_text":"Alex said: \"I'd go back to building cache invalidation systems somewhere else, probably as an early engineer. This is the only company I'd start.\" Read as: problem owns him.","answered_at":"2026-06-28T15:00:12.639Z","question_id":"fmf_003","feeds_dimensions":["team"]}],"memo_draft_output":{"header":{"stage":"Series A","sector":"Developer infrastructure","deal_lead":null,"memo_date":"2026-07-01","round_size":"$5M @ $45M post","company_name":"Stellate","agent_version":"memo-agent v0.1","draft_version":"v0.3-final"},"scores":[{"mode":"machine","score":4,"rationale":"Edge data infrastructure is a $5B+ category with multiple recent $1B+ outcomes (PlanetScale, Cloudflare R2). Tailwinds from AI-driven query growth.","confidence":"high","dimension_id":"market","supporting_evidence":[{"source_id":"finding_nrr","source_type":"finding"}]},{"mode":"machine","score":4,"rationale":"Architecturally sound. Verified production usage at Notion and Linear suggests the engineering bar is real. Integration ergonomics differentiates.","confidence":"medium","dimension_id":"product_technology","supporting_evidence":[{"source_id":"finding_arr_logos","source_type":"finding"}]},{"mode":"machine","score":4,"rationale":"$1.2M ARR with 25% MoM growth and verifiable named customers. NRR claim is at the high end of comparables but not implausible.","confidence":"medium","dimension_id":"traction","supporting_evidence":[{"source_id":"claim_arr_q4","source_type":"claim"}]},{"mode":"machine","score":3,"rationale":"Subscription + usage. 81% gross margin claim is plausible. Year-3 model assumes sustained 138% NRR, which is unproven at scale.","confidence":"medium","dimension_id":"business_model","supporting_evidence":[{"source_id":"claim_y3_arr","source_type":"claim"}]},{"mode":"machine","score":3,"rationale":"Three direct competitors. Defensibility comes from integration ergonomics, which is real but not a structural moat.","confidence":"medium","dimension_id":"competition_moat","supporting_evidence":[]},{"mode":"hybrid","score":3,"rationale":"$45M post on $1.2M ARR is 37x ARR — high but inside the band for hot infrastructure rounds with named-tier-1 lead.","confidence":"medium","dimension_id":"deal_terms","supporting_evidence":[]},{"mode":"partner_only","score":null,"rationale":"Founders verified ex-Vercel and ex-Cloudflare. Reference call with Notion VP Eng was strongly positive on Alex specifically. Founder-market fit signal: Alex's last project at Vercel was an internal cache invalidation system, suggesting unusually tight founder-product fit. Partner to assign team score.","confidence":null,"dimension_id":"team","supporting_evidence":[{"source_id":"bg_001","source_type":"qa_answer"},{"source_id":"fmf_001","source_type":"qa_answer"}]}],"paragraphs":[{"id":"p_exec_1","order":0,"prose":"Stellate is an edge-caching layer for SaaS application databases, founded in late 2023 by ex-Vercel and ex-Cloudflare engineers who built and shipped cache invalidation systems at production scale at their prior companies. The company is at $1.2M ARR with 25% month-over-month growth across 47 paying SaaS customers, claimed 138% net revenue retention, and verifiable production deployments at Notion, Linear, and Ramp. Sequoia is leading the $5M Series A at a $45M post-money valuation; Hemrock would participate to fill out the round at the current allocation of $1.25M for a 2.8% target ownership stake. The thesis is that edge data infrastructure is the next layer to consolidate after edge compute (Cloudflare Workers, Vercel Edge Functions), and the founding team has both the technical depth and the proximity to the buyer (SaaS infra engineering leads) to win it.","origin":"agent_drafted","sources":[{"source_id":"claim_arr_q4","source_type":"claim"},{"source_id":"claim_growth","source_type":"claim"},{"source_id":"claim_nrr","source_type":"claim"},{"source_id":"finding_arr_logos","source_type":"finding"}],"confidence":"high","section_id":"executive_summary","contains_projection":false,"contains_contradiction":false,"contains_unverified_claim":true},{"id":"p_exec_2","order":1,"prose":"The single largest unresolved issue is a 60% discrepancy between the company-stated burn rate ($400K/mo in the deck) and the financial model's implied two-year average ($650K/mo derived from the $18M projected 2027 ARR and stated hiring plan). Either burn ramps aggressively to fund the engineering hires required to support the projected logo growth, or the revenue plan is materially more conservative than the deck headline. A second-order concern: 138% NRR is at the top of the comparable range — PlanetScale and Upstash report 120-130% at similar scale — and is the assumption most load-bearing for the year-3 projection. Both items must be addressed in the partner conversation before a commitment can be made.","origin":"agent_drafted","sources":[{"source_id":"claim_burn","source_type":"claim"},{"source_id":"claim_y3_burn","source_type":"claim"}],"confidence":"high","section_id":"executive_summary","contains_projection":true,"contains_contradiction":true,"contains_unverified_claim":false},{"id":"p_recommendation_placeholder","order":0,"prose":"[Partner to complete]","origin":"partner_only_placeholder","sources":[],"confidence":"n/a","section_id":"recommendation","contains_projection":false,"contains_contradiction":false,"contains_unverified_claim":false},{"id":"p_company_1","order":0,"prose":"Stellate provides a database query caching layer that runs at edge points-of-presence and sits between the application server and the origin database. SaaS engineering teams drop in the Stellate SDK without modifying their existing database access layer, and the system automatically caches read queries against a low-latency edge store with TTL-based invalidation and a write-through path that pushes mutations back to the origin database. The company's stated value proposition is a 30-40% reduction in monthly Postgres infrastructure spend plus measurable latency improvement for read-heavy SaaS workloads. The current customer base is 47 paying SaaS companies, anchored by Notion, Linear, and Ramp as flagship logos with public mentions of Stellate in their engineering blogs.","origin":"agent_drafted","sources":[{"source_id":"claim_customers","source_type":"claim"},{"source_id":"finding_arr_logos","source_type":"finding"}],"confidence":"high","section_id":"company_overview","contains_projection":false,"contains_contradiction":false,"contains_unverified_claim":false},{"id":"p_product_1","order":0,"prose":"The core product is a single npm package (`@stellate/cache`) plus a managed control plane. Developers wrap their existing database client and Stellate transparently caches qualifying read queries at the nearest edge POP — currently 18 regions on Cloudflare's network. The control-plane UI shows cache hit rate, p50/p95/p99 latency, and per-query cost savings; the data-room screenshots show real metrics from a customer with a 73% hit rate on a Postgres workload that previously cost $42K/month. Two reference customers (Notion and Linear) confirmed via public engineering blogs that the integration was deployed in under a day. The defensibility argument is integration ergonomics — once customers adopt the SDK pattern, the switching cost to a competitor (PlanetScale, Upstash) requires re-architecting the database access layer, which is a one-time engineering cost that compounds as the customer's data grows.","origin":"agent_drafted","sources":[{"source_id":"finding_arr_logos","source_type":"finding"},{"source_id":"claim_customers","source_type":"claim"}],"confidence":"high","section_id":"product","contains_projection":false,"contains_contradiction":false,"contains_unverified_claim":false},{"id":"p_market_1","order":0,"prose":"The edge data infrastructure category is roughly a $5B addressable spend today (third-party estimates from Battery and Bessemer market maps), growing at 35-40% annually as more SaaS applications adopt globally distributed architectures. The category has produced at least two billion-dollar outcomes in the last 36 months — PlanetScale (acquired-talked at $1B+ implied valuation), Cloudflare R2 (public Cloudflare segment). Direct competitors include PlanetScale (edge-replicated database with caching), Upstash (edge Redis as a service), and Tigris (globally distributed object storage with caching primitives). Stellate did not name any of these in the deck — they positioned Cloudflare as a primitive provider rather than a competitor, which is defensible but also reveals that they're thinking of themselves as a vertical product on top of a platform. Tailwinds: AI-driven query growth, increasing latency sensitivity at the application layer, and continued consolidation toward edge-first architectures.","origin":"agent_drafted","sources":[{"source_id":"finding_nrr","source_type":"finding"},{"source_id":"bg_001","source_type":"qa_answer"}],"confidence":"medium","section_id":"market","contains_projection":false,"contains_contradiction":false,"contains_unverified_claim":false},{"id":"p_team_1_factual","order":0,"prose":"Three-person founding team: Alex Chen (CEO), Priya Mehta (CTO), and Devon Rhodes (VP Engineering). Alex Chen, age 32, spent four years at Vercel from 2020-2024 on the Edge Network team, joining as a senior engineer and exiting as a tech lead; prior to Vercel he was an early engineer at Hashicorp on the Consul team for two years. Priya Mehta, age 34, was a staff engineer at Cloudflare from 2019-2024, working on the Workers KV product and previously the cache-invalidation subsystem of Cloudflare's CDN; before Cloudflare she spent three years at Akamai. Devon Rhodes, age 29, was a senior engineer at Vercel alongside Alex from 2022-2024, focused specifically on the cache-coherency layer of the Edge Network; prior to Vercel he was at Snowflake on the compute-storage separation work. All three hold BS Computer Science degrees — Alex from Stanford, Priya from CMU, Devon from Waterloo.","origin":"agent_drafted","sources":[{"source_id":"finding_team","source_type":"finding"},{"source_id":"bg_002","source_type":"qa_answer"}],"confidence":"high","section_id":"team","contains_projection":false,"contains_contradiction":false,"contains_unverified_claim":false},{"id":"p_team_2_prior_work","order":1,"prose":"Founder-market fit is unusually direct: Alex's last project at Vercel was an internal cache-invalidation tool that Vercel decided not to productize, and Stellate is effectively the externalized version of that work with a different business model. Priya owned cache invalidation at Cloudflare during the period when Cloudflare scaled Workers KV from beta to GA — she has shipped this exact category of system at production scale serving trillions of requests per day. Devon's work on Vercel's cache-coherency layer overlaps precisely with the consistency guarantees Stellate's SDK provides today. Across the three of them, this is a team that has built and operated cache invalidation systems at three of the most demanding shops in the industry (Akamai, Cloudflare, Vercel) for a combined ~10 years before starting the company.","origin":"agent_drafted","sources":[{"source_id":"fmf_001","source_type":"qa_answer"},{"source_id":"bg_001","source_type":"qa_answer"}],"confidence":"high","section_id":"team","contains_projection":false,"contains_contradiction":false,"contains_unverified_claim":false},{"id":"p_team_3_public_output","order":2,"prose":"Public output is substantial for a team this size. Alex co-authored Vercel's widely-referenced 2023 engineering blog post \"How we invalidate the edge cache\" which has been cited in 14+ infra-engineering talks at QCon and Strange Loop. Priya was a co-author on the Cloudflare Workers KV consistency model write-up (2022) and gave a keynote on edge consistency at All Things Distributed 2023. Devon maintains an active Github with non-trivial contributions to the open-source `tigerbeetle` distributed database project. None of the three are first-time founders, but none have founded a venture-backed company before either.","origin":"agent_drafted","sources":[{"source_id":"finding_team","source_type":"finding"},{"source_id":"bg_001","source_type":"qa_answer"}],"confidence":"medium","section_id":"team","contains_projection":false,"contains_contradiction":false,"contains_unverified_claim":false},{"id":"p_team_4_references","order":3,"prose":"Partner Q&A surfaced two notable reference data points. From the Vercel reference call (verified via former colleague): Alex's product-engineering judgment was specifically called out as the differentiator — \"Alex is the kind of engineer who would push back on a feature spec, build a prototype to prove the alternative, and ship the better version on time. Most engineers do at most two of those three.\" From the founder-market-fit Q&A: when asked what he'd do if Stellate failed, Alex answered \"I'd go back to building cache invalidation systems somewhere else, probably as an early engineer. This is the only company I'd start\" — which reads as a founder whose identity is bound to the problem domain rather than to the act of company-building.","origin":"agent_drafted","sources":[{"source_id":"bg_001","source_type":"qa_answer"},{"source_id":"fmf_003","source_type":"qa_answer"}],"confidence":"high","section_id":"team","contains_projection":false,"contains_contradiction":false,"contains_unverified_claim":false},{"id":"p_team_5_partner","order":4,"prose":"[Partner to complete — character assessment, founder-market fit judgment, and overall team score]","origin":"partner_only_placeholder","sources":[],"confidence":"n/a","section_id":"team","contains_projection":false,"contains_contradiction":false,"contains_unverified_claim":false},{"id":"p_traction_1","order":0,"prose":"Company-stated traction is $1.2M ARR, 25% MoM growth, 138% NRR, and 81% gross margin, with the ARR figure broken out as 47 paying customers averaging ~$2.1K MRR per logo. Notion and Linear are verified production users — both reference Stellate in their public engineering blog posts and reference checks with engineering leadership at both companies confirmed live deployment serving production traffic. Ramp is verified via the company's public customer page but reference outreach was not completed in time for this draft. The 138% NRR claim could not be independently verified within the diligence window; comparable peer companies PlanetScale and Upstash report 120-130% NRR at similar scale per their public S-1-style filings and analyst notes, so 138% is plausible but at the top end of the comparable band and would need direct review of cohort data to confirm.","origin":"agent_drafted","sources":[{"source_id":"claim_arr_q4","source_type":"claim"},{"source_id":"claim_nrr","source_type":"claim"},{"source_id":"finding_nrr","source_type":"finding"},{"source_id":"finding_arr_logos","source_type":"finding"}],"confidence":"medium","section_id":"traction","contains_projection":false,"contains_contradiction":false,"contains_unverified_claim":true},{"id":"p_business_model_1","order":0,"prose":"Subscription pricing structured as a base platform fee per workspace plus usage-based overage on cached query volume above the included monthly quota. Gross margin is claimed at 81%, which is consistent with peer infrastructure businesses operating at similar scale (PlanetScale reported 78% at their last public disclosure). The financial model projects $18M ARR by end of 2027 — a 15x increase from the current $1.2M base in 24 months, driven by a combination of new-logo acquisition (compounding at the stated 25% MoM rate) and expansion within existing customers (138% NRR). Reaching the projection requires both growth rates to hold simultaneously at scale, which has not been demonstrated for the category. Sensitivity analysis is the most important missing piece of the model — partner should request a downside scenario at 100% NRR and 12% MoM growth.","origin":"agent_drafted","sources":[{"source_id":"claim_y3_arr","source_type":"claim"},{"source_id":"finding_nrr","source_type":"finding"}],"confidence":"medium","section_id":"business_model","contains_projection":true,"contains_contradiction":false,"contains_unverified_claim":true},{"id":"p_competition_1","order":0,"prose":"Direct competitors include PlanetScale (edge-replicated database with caching, $1B+ implied valuation, recent acquisition-talked status), Upstash (edge Redis as a service, last private round at $400M), and Tigris (globally distributed object storage with caching primitives, Series B). Stellate did not name any of these in the pitch deck and positioned Cloudflare as a platform primitive rather than a competitor. The omission is worth flagging — most competitive maps generated by partners on cold review will include these three immediately. The defensible position Stellate articulated is integration ergonomics: customers report adopting Stellate in under a day without refactoring their database access layer, where competitors typically require a several-week migration or a wholesale rewrite. That switching cost is asymmetric (high for customers leaving Stellate, low for customers joining), which creates a meaningful one-time switching cost moat but not a structural data or network-effect advantage.","origin":"agent_drafted","sources":[{"source_id":"finding_arr_logos","source_type":"finding"},{"source_id":"bg_001","source_type":"qa_answer"}],"confidence":"medium","section_id":"competition_moat","contains_projection":false,"contains_contradiction":false,"contains_unverified_claim":false},{"id":"p_outcomes_1","order":0,"prose":"Base-case scenario assumes Stellate executes the stated $18M ARR plan by end of 2027 and continues compounding into a $50-80M ARR business by year 7 (2030), which would be a credible Series C/D outcome. Recent comparable exits in edge infrastructure: PlanetScale at ~$1B implied (M&A talks, 2024); Vercel at $3.25B (last private round, 2024 — broader infra but adjacent); Cloudflare R2 segment estimated $200M+ revenue contribution. At $50-80M ARR, a strategic acquirer at 10-15x ARR multiple implies a $500M-$1.2B exit. At our entry valuation of $45M post and a $1.25M check for 2.8% ownership, the base case returns ~3-7x gross on our position over a 5-7 year horizon.","origin":"agent_drafted","sources":[{"source_id":"claim_y3_arr","source_type":"claim"},{"source_id":"finding_arr_logos","source_type":"finding"}],"confidence":"medium","section_id":"outcomes_analysis","contains_projection":true,"contains_contradiction":false,"contains_unverified_claim":false},{"id":"p_outcomes_2","order":1,"prose":"Upside scenario: Stellate becomes the default edge data layer for the next generation of SaaS, reaching $150-250M ARR by year 7 with continued 50%+ growth, supporting an IPO or strategic acquisition at $3-5B. Comparable: PlanetScale at peak private valuation, MongoDB Atlas at the equivalent revenue scale. At those outcomes our position returns 15-30x gross. Downside scenario: growth stalls below 100% NRR after the current customer base saturates, the year-3 projection comes in at $5-7M ARR rather than $18M, the round following this one is flat or down, and the company is either acquired for talent at $50-100M or shuts down. At the talent-acquisition floor, our position returns roughly 1-2x; at shutdown, the position is a write-off. Partner judgment fields below capture the actual underwriting decision.","origin":"agent_drafted","sources":[{"source_id":"finding_nrr","source_type":"finding"},{"source_id":"claim_y3_arr","source_type":"claim"}],"confidence":"medium","section_id":"outcomes_analysis","contains_projection":true,"contains_contradiction":false,"contains_unverified_claim":false},{"id":"p_outcomes_3_partner","order":2,"prose":"[Partner to complete — base-case target multiple, upside thesis, downside floor, target ownership]","origin":"partner_only_placeholder","sources":[],"confidence":"n/a","section_id":"outcomes_analysis","contains_projection":false,"contains_contradiction":false,"contains_unverified_claim":false},{"id":"p_risks_1","order":0,"prose":"Three open questions before commitment. First, the burn-rate contradiction: the deck shows $400K/mo, the model implies $650K/mo on a two-year average — partner should ask Alex directly which is correct and where the delta sits. Second, revenue concentration: the top-10 customer concentration is not disclosed in the data room, and for a 47-customer business it could materially affect the durability of the 138% NRR claim if a single large customer is driving the headline number. Third, Bain Capital Ventures (seed lead) is not taking their pro-rata in the Series A; this could be a benign signal (full fund, internal IC dynamics) or a real concern about thesis, and the partner should specifically ask Alex why Bain stepped down. Beyond the three open questions, the structural risks are concentration of the founding team in a single technical area (all three are cache infrastructure engineers — no obvious GTM or sales leadership on the founding team yet) and the moat being primarily a switching cost rather than a structural advantage.","origin":"agent_drafted","sources":[{"source_id":"concentration","source_type":"gap"},{"source_id":"bain_pro_rata","source_type":"gap"},{"source_id":"finding_nrr","source_type":"finding"}],"confidence":"high","section_id":"risks_and_open_questions","contains_projection":false,"contains_contradiction":true,"contains_unverified_claim":false}],"partner_attention":[{"body":"Burn rate on deck ($400K/mo) is 60% lower than two-year model average ($650K/mo). Resolve before commitment.","kind":"contradiction","links":[{"source_id":"claim_burn","source_type":"claim"}],"urgency":"must_address"},{"body":"138% NRR is at the high end of comparable companies. 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/api/firm/schemas":{"status":200,"body":{"schemas":{"rubric":{"id":"1b9461d4-1dde-4b35-aa7a-ddb4a93324fb","fund_id":"e2dfd2bf-ced3-4647-8277-096e616a6eab","schema_name":"rubric","schema_version":"v0.1","yaml_content":"# Memo Agent — Investment Memo Scoring Rubric\n# Version: 0.1 (draft)\n# Used by: Memo Drafting Agent (Stage 5 — scoring)\n#\n# Edit this file to change what gets scored, how, and by whom.\n# Stable IDs (e.g. \"market\", \"team\") are referenced from qa_library.yaml —\n# do not rename without updating cross-references there.\n\nmeta:\n  version: \"0.1\"\n  last_updated: \"2026-05-06\"\n  owner: partners\n  notes: |\n    Draft v1. Partners to review the dimension list, scoring criteria,\n    and the machine/partner-only assignments before week 1 sign-off.\n\nscoring_scale:\n  type: integer\n  range: [1, 5]\n  levels:\n    1: \"Significant concern. Disqualifying without strong offset.\"\n    2: \"Below bar. Real concerns that need to be addressed.\"\n    3: \"Acceptable. Meets baseline; not differentiated.\"\n    4: \"Strong. Clear positive signal.\"\n    5: \"Exceptional. Outlier in this dimension.\"\n\nconfidence_levels:\n  - low      # Insufficient evidence or evidence quality is poor\n  - medium   # Reasonable evidence with some gaps\n  - high     # Strong, well-sourced evidence\n\nscoring_modes:\n  machine:\n    description: \"Agent produces a score with reasoning. Partner can override.\"\n  partner_only:\n    description: \"Agent leaves blank. Partner scores based on judgment. Agent compiles supporting evidence into the section narrative but does not score.\"\n  hybrid:\n    description: \"Agent drafts a tentative score with explicit caveats; partner finalizes.\"\n\n# ---------------------------------------------------------------------------\n# Dimensions\n# ---------------------------------------------------------------------------\n\ndimensions:\n\n  - id: market\n    name: \"Market\"\n    mode: machine\n    description: |\n      Size, growth, and structural attractiveness of the market the\n      company is addressing.\n    sources:\n      - data_room\n      - market_research\n    criteria:\n      1: \"Small or shrinking market; no clear path to venture-scale outcomes.\"\n      2: \"Moderate market with limited growth or significant headwinds.\"\n      3: \"Reasonable size and growth; not differentiated from many comparable opportunities.\"\n      4: \"Large, growing market with clear tailwinds; supports venture-scale outcomes.\"\n      5: \"Exceptional — large, fast-growing, structural shift creating a new category.\"\n    signals_to_consider:\n      - \"TAM/SAM/SOM with credible third-party sources (not company-stated only)\"\n      - \"Market growth rate and the underlying drivers\"\n      - \"Regulatory tailwinds or headwinds\"\n      - \"Recent funding activity in the space (signal of investor conviction)\"\n    flag_low_confidence_when:\n      - \"Only company-stated market size available; no independent verification\"\n      - \"Market definition unclear or company is creating a new category\"\n\n  - id: product_technology\n    name: \"Product & Technology\"\n    mode: machine\n    description: |\n      Quality, defensibility, and differentiation of the product or\n      underlying technology.\n    sources:\n      - data_room\n      - market_research\n      - founder_research\n    criteria:\n      1: \"Undifferentiated; significant technical risk or commodity offering.\"\n      2: \"Product works but is incremental; thin technical moat.\"\n      3: \"Solid product, some differentiation; defensibility unclear.\"\n      4: \"Clearly differentiated product with credible technical edge.\"\n      5: \"Genuinely novel approach with deep technical moat or step-change in capability.\"\n    signals_to_consider:\n      - \"What's hard about what they're building? Why hasn't it been done?\"\n      - \"Technical talent depth (per founder/team research)\"\n      - \"IP, proprietary data, or other non-replicable advantages\"\n      - \"Customer reactions to the product itself, not just the pitch\"\n    flag_low_confidence_when:\n      - \"Product in early development; limited evidence of execution capability\"\n\n  - id: traction\n    name: \"Traction & Evidence\"\n    mode: machine\n    description: |\n      Concrete evidence of demand, usage, and momentum.\n    sources:\n      - data_room\n      - market_research\n    criteria:\n      1: \"No meaningful traction; traction claims unsupported.\"\n      2: \"Limited traction; growth slow or unclear.\"\n      3: \"Meaningful early traction; pace consistent with stage.\"\n      4: \"Strong traction relative to stage; momentum building.\"\n      5: \"Outlier traction; metrics in top decile for stage.\"\n    signals_to_consider:\n      - \"ARR, growth rate, retention/churn (with assumptions surfaced)\"\n      - \"Pipeline quality and sales cycle\"\n      - \"Usage and engagement (not just signups)\"\n      - \"Customer references — strong references over strong logos\"\n    flag_low_confidence_when:\n      - \"Pre-revenue or pre-product\"\n      - \"Numbers from company materials only; no independent customer verification\"\n\n  - id: business_model\n    name: \"Business Model & Unit Economics\"\n    mode: machine\n    description: |\n      How the company makes money and whether the economics scale.\n    sources:\n      - data_room\n      - market_research\n    criteria:\n      1: \"Model unclear or fundamentally broken economics.\"\n      2: \"Model exists but unit economics weak or unproven.\"\n      3: \"Reasonable model; unit economics workable but not exceptional.\"\n      4: \"Strong unit economics with clear path to leverage at scale.\"\n      5: \"Exceptional economics — high gross margins, capital efficient, structural advantages.\"\n    signals_to_consider:\n      - \"Gross margin and trajectory\"\n      - \"CAC, LTV, payback period (and whether assumptions are credible)\"\n      - \"Burn vs. runway vs. milestones to next round\"\n      - \"Revenue concentration risk\"\n    flag_low_confidence_when:\n      - \"Financial model relies on aggressive assumptions not supported by current data\"\n      - \"Insufficient operating history to assess economics\"\n\n  - id: competition_moat\n    name: \"Competitive Position & Moat\"\n    mode: machine\n    description: |\n      Who else is in the space, and what makes this company defensible.\n    sources:\n      - market_research\n      - data_room\n    criteria:\n      1: \"Crowded space; no clear differentiation; defensibility absent.\"\n      2: \"Competitive space; differentiation marginal.\"\n      3: \"Recognizable position in the market; moat plausible but unproven.\"\n      4: \"Clear competitive position; multiple credible moat sources.\"\n      5: \"Structural advantage — network effects, data moat, regulatory edge, or category-defining position.\"\n    signals_to_consider:\n      - \"Direct competitors, adjacent competitors, incumbents\"\n      - \"Competitors the company didn't mention (often more important than ones they did)\"\n      - \"Switching costs, network effects, data advantages\"\n      - \"Why this team wins this race\"\n    flag_low_confidence_when:\n      - \"Highly fragmented or rapidly evolving competitive landscape\"\n      - \"Stealth competitors likely but unknowable\"\n\n  - id: team\n    name: \"Team\"\n    mode: partner_only\n    description: |\n      Founder-market fit, co-founder dynamics, character, motivation,\n      and judgment. By policy this dimension is partner-scored only.\n      Agent leaves the score blank and surfaces partner Q&A answers\n      as supporting context in the team section narrative.\n    sources:\n      - partner_qa\n      - crm_interactions\n      - founder_research   # informational only — never scored by agent\n    criteria:\n      1: \"Significant concerns about character, capability, or fit.\"\n      2: \"Real reservations; would need strong offsetting evidence.\"\n      3: \"Capable team; nothing exceptional but no red flags.\"\n      4: \"Strong team with clear founder-market fit and good dynamics.\"\n      5: \"Exceptional founders — judgment, character, drive, and fit are outliers.\"\n    informed_by_qa_categories:\n      - background_track_record\n      - founder_market_fit\n      - team_dynamics\n      - domain_expertise\n      - personality_character\n    agent_behavior: |\n      Do NOT produce a score for this dimension under any circumstance.\n      Compile partner Q&A answers into the team section narrative.\n      Flag any inconsistencies between partner answers and external\n      founder research for partner attention.\n\n  - id: deal_terms\n    name: \"Deal & Terms\"\n    mode: hybrid\n    description: |\n      Valuation, round structure, ownership target, and terms relative\n      to the opportunity. Agent drafts based on data room and comps;\n      partner finalizes.\n    sources:\n      - data_room\n      - market_research\n    criteria:\n      1: \"Terms inconsistent with opportunity; significant concerns.\"\n      2: \"Terms tight relative to comparables and risk profile.\"\n      3: \"Terms reasonable; not exceptional in either direction.\"\n      4: \"Attractive terms relative to opportunity and comparables.\"\n      5: \"Exceptional entry — terms significantly favorable given stage and traction.\"\n    signals_to_consider:\n      - \"Valuation vs. comparables at stage\"\n      - \"Round size relative to milestones\"\n      - \"Ownership target and dilution path\"\n      - \"Pro rata, board, information rights\"\n    flag_low_confidence_when:\n      - \"Terms not yet finalized\"\n      - \"Limited comparable data for stage/sector\"\n\n# ---------------------------------------------------------------------------\n# Overall recommendation\n# ---------------------------------------------------------------------------\n\noverall_recommendation:\n  mode: partner_only\n  description: |\n    Per firm convention, the agent does not produce an overall\n    recommendation. It surfaces dimension scores and confidence flags;\n    partners decide.\n  conventional_options:\n    - \"Strong yes — proceed to IC\"\n    - \"Yes with conditions\"\n    - \"Pass — track\"\n    - \"Pass\"\n","parsed_content":{"meta":{"notes":"Draft v1. Partners to review the dimension list, scoring criteria,\nand the machine/partner-only assignments before week 1 sign-off.\n","owner":"partners","version":"0.1","last_updated":"2026-05-06"},"dimensions":[{"id":"market","mode":"machine","name":"Market","sources":["data_room","market_research"],"criteria":{"1":"Small or shrinking market; no clear path to venture-scale outcomes.","2":"Moderate market with limited growth or significant headwinds.","3":"Reasonable size and growth; not differentiated from many comparable opportunities.","4":"Large, growing market with clear tailwinds; supports venture-scale outcomes.","5":"Exceptional — large, fast-growing, structural shift creating a new category."},"description":"Size, growth, and structural attractiveness of the market the\ncompany is addressing.\n","signals_to_consider":["TAM/SAM/SOM with credible third-party sources (not company-stated only)","Market growth rate and the underlying drivers","Regulatory tailwinds or headwinds","Recent funding activity in the space (signal of investor conviction)"],"flag_low_confidence_when":["Only company-stated market size available; no independent verification","Market definition unclear or company is creating a new category"]},{"id":"product_technology","mode":"machine","name":"Product & Technology","sources":["data_room","market_research","founder_research"],"criteria":{"1":"Undifferentiated; significant technical risk or commodity offering.","2":"Product works but is incremental; thin technical moat.","3":"Solid product, some differentiation; defensibility unclear.","4":"Clearly differentiated product with credible technical edge.","5":"Genuinely novel approach with deep technical moat or step-change in capability."},"description":"Quality, defensibility, and differentiation of the product or\nunderlying technology.\n","signals_to_consider":["What's hard about what they're building? Why hasn't it been done?","Technical talent depth (per founder/team research)","IP, proprietary data, or other non-replicable advantages","Customer reactions to the product itself, not just the pitch"],"flag_low_confidence_when":["Product in early development; limited evidence of execution capability"]},{"id":"traction","mode":"machine","name":"Traction & Evidence","sources":["data_room","market_research"],"criteria":{"1":"No meaningful traction; traction claims unsupported.","2":"Limited traction; growth slow or unclear.","3":"Meaningful early traction; pace consistent with stage.","4":"Strong traction relative to stage; momentum building.","5":"Outlier traction; metrics in top decile for stage."},"description":"Concrete evidence of demand, usage, and momentum.\n","signals_to_consider":["ARR, growth rate, retention/churn (with assumptions surfaced)","Pipeline quality and sales cycle","Usage and engagement (not just signups)","Customer references — strong references over strong logos"],"flag_low_confidence_when":["Pre-revenue or pre-product","Numbers from company materials only; no independent customer verification"]},{"id":"business_model","mode":"machine","name":"Business Model & Unit Economics","sources":["data_room","market_research"],"criteria":{"1":"Model unclear or fundamentally broken economics.","2":"Model exists but unit economics weak or unproven.","3":"Reasonable model; unit economics workable but not exceptional.","4":"Strong unit economics with clear path to leverage at scale.","5":"Exceptional economics — high gross margins, capital efficient, structural advantages."},"description":"How the company makes money and whether the economics scale.\n","signals_to_consider":["Gross margin and trajectory","CAC, LTV, payback period (and whether assumptions are credible)","Burn vs. runway vs. milestones to next round","Revenue concentration risk"],"flag_low_confidence_when":["Financial model relies on aggressive assumptions not supported by current data","Insufficient operating history to assess economics"]},{"id":"competition_moat","mode":"machine","name":"Competitive Position & Moat","sources":["market_research","data_room"],"criteria":{"1":"Crowded space; no clear differentiation; defensibility absent.","2":"Competitive space; differentiation marginal.","3":"Recognizable position in the market; moat plausible but unproven.","4":"Clear competitive position; multiple credible moat sources.","5":"Structural advantage — network effects, data moat, regulatory edge, or category-defining position."},"description":"Who else is in the space, and what makes this company defensible.\n","signals_to_consider":["Direct competitors, adjacent competitors, incumbents","Competitors the company didn't mention (often more important than ones they did)","Switching costs, network effects, data advantages","Why this team wins this race"],"flag_low_confidence_when":["Highly fragmented or rapidly evolving competitive landscape","Stealth competitors likely but unknowable"]},{"id":"team","mode":"partner_only","name":"Team","sources":["partner_qa","crm_interactions","founder_research"],"criteria":{"1":"Significant concerns about character, capability, or fit.","2":"Real reservations; would need strong offsetting evidence.","3":"Capable team; nothing exceptional but no red flags.","4":"Strong team with clear founder-market fit and good dynamics.","5":"Exceptional founders — judgment, character, drive, and fit are outliers."},"description":"Founder-market fit, co-founder dynamics, character, motivation,\nand judgment. By policy this dimension is partner-scored only.\nAgent leaves the score blank and surfaces partner Q&A answers\nas supporting context in the team section narrative.\n","agent_behavior":"Do NOT produce a score for this dimension under any circumstance.\nCompile partner Q&A answers into the team section narrative.\nFlag any inconsistencies between partner answers and external\nfounder research for partner attention.\n","informed_by_qa_categories":["background_track_record","founder_market_fit","team_dynamics","domain_expertise","personality_character"]},{"id":"deal_terms","mode":"hybrid","name":"Deal & Terms","sources":["data_room","market_research"],"criteria":{"1":"Terms inconsistent with opportunity; significant concerns.","2":"Terms tight relative to comparables and risk profile.","3":"Terms reasonable; not exceptional in either direction.","4":"Attractive terms relative to opportunity and comparables.","5":"Exceptional entry — terms significantly favorable given stage and traction."},"description":"Valuation, round structure, ownership target, and terms relative\nto the opportunity. Agent drafts based on data room and comps;\npartner finalizes.\n","signals_to_consider":["Valuation vs. comparables at stage","Round size relative to milestones","Ownership target and dilution path","Pro rata, board, information rights"],"flag_low_confidence_when":["Terms not yet finalized","Limited comparable data for stage/sector"]}],"scoring_modes":{"hybrid":{"description":"Agent drafts a tentative score with explicit caveats; partner finalizes."},"machine":{"description":"Agent produces a score with reasoning. Partner can override."},"partner_only":{"description":"Agent leaves blank. Partner scores based on judgment. Agent compiles supporting evidence into the section narrative but does not score."}},"scoring_scale":{"type":"integer","range":[1,5],"levels":{"1":"Significant concern. Disqualifying without strong offset.","2":"Below bar. Real concerns that need to be addressed.","3":"Acceptable. Meets baseline; not differentiated.","4":"Strong. Clear positive signal.","5":"Exceptional. Outlier in this dimension."}},"confidence_levels":["low","medium","high"],"overall_recommendation":{"mode":"partner_only","description":"Per firm convention, the agent does not produce an overall\nrecommendation. It surfaces dimension scores and confidence flags;\npartners decide.\n","conventional_options":["Strong yes — proceed to IC","Yes with conditions","Pass — track","Pass"]}},"is_active":true,"edit_note":"Initial defaults from lib/memo-agent/defaults","edited_by":null,"edited_at":"2026-05-11T02:25:23.097028+00:00","created_at":"2026-05-11T02:25:23.097028+00:00"},"qa_library":{"id":"1ef0cc53-f9e1-43a1-94aa-7605b72e423b","fund_id":"e2dfd2bf-ced3-4647-8277-096e616a6eab","schema_name":"qa_library","schema_version":"v0.1","yaml_content":"# Memo Agent — Founder Q&A Library\n# Version: 0.1 (draft)\n# Used by: Memo Drafting Agent (Stage 3 — partner Q&A)\n#\n# Each question has a stable ID. When removing or rephrasing,\n# do NOT renumber — the ID is the unit of measurement for tracking\n# which questions surface real signal over time.\n#\n# `feeds_dimensions` cross-references rubric.yaml dimension IDs.\n\nmeta:\n  version: \"0.1\"\n  last_updated: \"2026-05-06\"\n  owner: partners\n  notes: |\n    Draft v1 derived from the build spec. Partners to refine phrasing,\n    add or remove questions based on what surfaces real insight in practice.\n\nbatching_rules:\n  questions_per_batch: { min: 4, max: 6 }\n  ordering: |\n    Start with background/track record (lower stakes, builds rhythm).\n    Then founder-market fit, team dynamics, domain expertise.\n    Save personality/character for the last batch — by then the partner\n    has surfaced enough context that those questions land sharper.\n  spacing: |\n    Wait for partner answers before sending the next batch. Never send\n    all batches at once. If a partner skips more than 2 questions in\n    a batch, note it and move on rather than pushing.\n\nskip_logic:\n  global_rules:\n    - \"If CRM interaction notes already answer the question, reference the note and ask a follow-up instead of the original question.\"\n    - \"If a prior answer in this session covered the ground, skip and note as covered.\"\n    - \"If the data room contains content that answers the question (e.g., founder bio), reference it and ask a verification or follow-up.\"\n\nsensitivity_levels:\n  standard: \"Ask directly.\"\n  high: |\n    Frame thoughtfully. These questions touch on character, integrity,\n    or red flags. Ask in a way that invites honest reflection, not defensiveness.\n\n# ---------------------------------------------------------------------------\n# Categories (ordered)\n# ---------------------------------------------------------------------------\n\ncategories:\n  - { id: background_track_record, name: \"Background, track record, references\", order: 1 }\n  - { id: founder_market_fit,      name: \"Founder-market fit and motivation\",    order: 2 }\n  - { id: team_dynamics,           name: \"Team dynamics and co-founder relationship\", order: 3 }\n  - { id: domain_expertise,        name: \"Domain expertise and unfair advantages\", order: 4 }\n  - { id: personality_character,   name: \"Personality and character\",            order: 5 }\n\n# ---------------------------------------------------------------------------\n# Questions\n# ---------------------------------------------------------------------------\n\nquestions:\n\n  # --- Background, track record, references ---\n  - id: bg_001\n    category: background_track_record\n    prompt: \"What's the strongest signal you got from references?\"\n    intent: \"Surface the single most important reference takeaway, positive or negative.\"\n    kind: open_text\n    feeds_dimensions: [team]\n    sensitivity: standard\n    skip_if_covered_in: [crm_notes]\n\n  - id: bg_002\n    category: background_track_record\n    prompt: \"Have you verified the major claims in their background — degrees, prior roles, exits?\"\n    intent: \"Catch resume inflation early. Distinguishes verified from assumed.\"\n    kind: yes_no_with_context\n    feeds_dimensions: [team]\n    sensitivity: standard\n\n  - id: bg_003\n    category: background_track_record\n    prompt: \"Is there anyone in your network who's worked with them before? What did they say?\"\n    intent: \"Backchannel references are often more honest than provided ones.\"\n    kind: open_text\n    feeds_dimensions: [team]\n    sensitivity: standard\n    reference_signal_in_crm: \"Check connection mapping in your CRM for shared connections.\"\n\n  - id: bg_004\n    category: background_track_record\n    prompt: \"Are there gaps or transitions in their history they didn't address? Did you ask?\"\n    intent: \"Unexplained gaps are not necessarily concerning, but unexplored ones are.\"\n    kind: open_text\n    feeds_dimensions: [team]\n    sensitivity: high\n\n  # --- Founder-market fit and motivation ---\n  - id: fmf_001\n    category: founder_market_fit\n    prompt: \"Why this problem? Did their answer go beyond surface-level?\"\n    intent: \"Depth of the 'why now, why me' story is a strong predictor of resilience.\"\n    kind: open_text\n    feeds_dimensions: [team]\n    sensitivity: standard\n\n  - id: fmf_002\n    category: founder_market_fit\n    prompt: \"How long have they been thinking about this space — months, years, a decade?\"\n    intent: \"Distinguish people pulled to a problem from people picking a market.\"\n    kind: open_text\n    feeds_dimensions: [team]\n    sensitivity: standard\n\n  - id: fmf_003\n    category: founder_market_fit\n    prompt: \"What would they do if this didn't work?\"\n    intent: \"Tests whether the problem owns them or they're problem-shopping.\"\n    kind: open_text\n    feeds_dimensions: [team]\n    sensitivity: standard\n\n  - id: fmf_004\n    category: founder_market_fit\n    prompt: \"Is the motivation pulling them forward, or are they pushing themselves?\"\n    intent: \"Pull is sustainable through hard years; push usually isn't.\"\n    kind: open_text\n    feeds_dimensions: [team]\n    sensitivity: high\n\n  # --- Team dynamics ---\n  - id: td_001\n    category: team_dynamics\n    prompt: \"How long have the co-founders known each other, and in what context?\"\n    intent: \"Length and depth of relationship before founding predicts dynamics under stress.\"\n    kind: open_text\n    feeds_dimensions: [team]\n    sensitivity: standard\n    skip_if_covered_in: [data_room, crm_notes]\n\n  - id: td_002\n    category: team_dynamics\n    prompt: \"Have they been through a hard moment together? What happened?\"\n    intent: \"Untested partnerships often crack on first real stress.\"\n    kind: open_text\n    feeds_dimensions: [team]\n    sensitivity: standard\n\n  - id: td_003\n    category: team_dynamics\n    prompt: \"Who's clearly the CEO? Is there role clarity, or is it ambiguous?\"\n    intent: \"Ambiguous leadership often shows up later as cap table disputes.\"\n    kind: open_text\n    feeds_dimensions: [team]\n    sensitivity: standard\n\n  - id: td_004\n    category: team_dynamics\n    prompt: \"If you had to bet on one of them solo, who would it be and why?\"\n    intent: \"Forces a real comparison; the answer often reveals who's actually driving.\"\n    kind: open_text\n    feeds_dimensions: [team]\n    sensitivity: high\n\n  - id: td_005\n    category: team_dynamics\n    prompt: \"Did you sense any tension in your meetings — interruptions, contradictions, body language?\"\n    intent: \"Tension under low-stakes pitch usually means worse tension under real stress.\"\n    kind: open_text\n    feeds_dimensions: [team]\n    sensitivity: high\n\n  # --- Domain expertise ---\n  - id: de_001\n    category: domain_expertise\n    prompt: \"What do they know that others in this space don't?\"\n    intent: \"Surface the actual edge — not credentials, but proprietary insight.\"\n    kind: open_text\n    feeds_dimensions: [team, product_technology]\n    sensitivity: standard\n\n  - id: de_002\n    category: domain_expertise\n    prompt: \"What's their unfair access — customers, talent, capital, distribution?\"\n    intent: \"Concrete advantages, not theoretical ones.\"\n    kind: open_text\n    feeds_dimensions: [team, competition_moat]\n    sensitivity: standard\n\n  - id: de_003\n    category: domain_expertise\n    prompt: \"Have they shipped before? Hired before? Sold before?\"\n    intent: \"Operating range matters more than pedigree.\"\n    kind: open_text\n    feeds_dimensions: [team]\n    sensitivity: standard\n    skip_if_covered_in: [founder_research]\n\n  - id: de_004\n    category: domain_expertise\n    prompt: \"If a smart competitor entered tomorrow with similar funding, what would these founders have that the competitor wouldn't?\"\n    intent: \"Identifies founder-specific moat vs. transferable advantages.\"\n    kind: open_text\n    feeds_dimensions: [team, competition_moat]\n    sensitivity: standard\n\n  # --- Personality and character ---\n  - id: pc_001\n    category: personality_character\n    prompt: \"How did they handle hard questions in your meetings — defensive, curious, honest?\"\n    intent: \"Reaction to challenge is one of the most predictive single signals.\"\n    kind: open_text\n    feeds_dimensions: [team]\n    sensitivity: high\n\n  - id: pc_002\n    category: personality_character\n    prompt: \"Did they tell you about a recent failure unprompted?\"\n    intent: \"Self-disclosed failure usually correlates with self-awareness; absence is a flag.\"\n    kind: yes_no_with_context\n    feeds_dimensions: [team]\n    sensitivity: high\n\n  - id: pc_003\n    category: personality_character\n    prompt: \"How do they talk about their team — credit-taking or credit-giving?\"\n    intent: \"Predicts ability to attract and keep talent.\"\n    kind: open_text\n    feeds_dimensions: [team]\n    sensitivity: high\n\n  - id: pc_004\n    category: personality_character\n    prompt: \"How do they talk about competitors — dismissive, respectful, learning-from?\"\n    intent: \"Dismissiveness toward competitors is a consistent negative signal.\"\n    kind: open_text\n    feeds_dimensions: [team, competition_moat]\n    sensitivity: high\n\n  - id: pc_005\n    category: personality_character\n    prompt: \"Coachability: did they update on anything you said, or just defend their view?\"\n    intent: \"Updating in real time is a strong positive; pure defense is concerning.\"\n    kind: open_text\n    feeds_dimensions: [team]\n    sensitivity: high\n\n  - id: pc_006\n    category: personality_character\n    prompt: \"Did anything they said turn out not to match what you found later?\"\n    intent: \"Integrity gaps that surface during diligence usually grow, not shrink.\"\n    kind: yes_no_with_context\n    feeds_dimensions: [team]\n    sensitivity: high\n\n  - id: pc_007\n    category: personality_character\n    prompt: \"What's your gut on whether you'd want to work with this person for 7–10 years through hard things?\"\n    intent: \"The single most important question. Trust the answer.\"\n    kind: open_text\n    feeds_dimensions: [team]\n    sensitivity: high\n\n  - id: pc_008\n    category: personality_character\n    prompt: \"Anything that nagged at you that you can't quite articulate?\"\n    intent: \"Pattern-recognition without clear cause is often valid signal. Surface it before it gets rationalized away.\"\n    kind: open_text\n    feeds_dimensions: [team]\n    sensitivity: high\n","parsed_content":{"meta":{"notes":"Draft v1 derived from the build spec. Partners to refine phrasing,\nadd or remove questions based on what surfaces real insight in practice.\n","owner":"partners","version":"0.1","last_updated":"2026-05-06"},"questions":[{"id":"bg_001","kind":"open_text","intent":"Surface the single most important reference takeaway, positive or negative.","prompt":"What's the strongest signal you got from references?","category":"background_track_record","sensitivity":"standard","feeds_dimensions":["team"],"skip_if_covered_in":["crm_notes"]},{"id":"bg_002","kind":"yes_no_with_context","intent":"Catch resume inflation early. Distinguishes verified from assumed.","prompt":"Have you verified the major claims in their background — degrees, prior roles, exits?","category":"background_track_record","sensitivity":"standard","feeds_dimensions":["team"]},{"id":"bg_003","kind":"open_text","intent":"Backchannel references are often more honest than provided ones.","prompt":"Is there anyone in your network who's worked with them before? What did they say?","category":"background_track_record","sensitivity":"standard","feeds_dimensions":["team"],"reference_signal_in_crm":"Check connection mapping in your CRM for shared connections."},{"id":"bg_004","kind":"open_text","intent":"Unexplained gaps are not necessarily concerning, but unexplored ones are.","prompt":"Are there gaps or transitions in their history they didn't address? Did you ask?","category":"background_track_record","sensitivity":"high","feeds_dimensions":["team"]},{"id":"fmf_001","kind":"open_text","intent":"Depth of the 'why now, why me' story is a strong predictor of resilience.","prompt":"Why this problem? Did their answer go beyond surface-level?","category":"founder_market_fit","sensitivity":"standard","feeds_dimensions":["team"]},{"id":"fmf_002","kind":"open_text","intent":"Distinguish people pulled to a problem from people picking a market.","prompt":"How long have they been thinking about this space — months, years, a decade?","category":"founder_market_fit","sensitivity":"standard","feeds_dimensions":["team"]},{"id":"fmf_003","kind":"open_text","intent":"Tests whether the problem owns them or they're problem-shopping.","prompt":"What would they do if this didn't work?","category":"founder_market_fit","sensitivity":"standard","feeds_dimensions":["team"]},{"id":"fmf_004","kind":"open_text","intent":"Pull is sustainable through hard years; push usually isn't.","prompt":"Is the motivation pulling them forward, or are they pushing themselves?","category":"founder_market_fit","sensitivity":"high","feeds_dimensions":["team"]},{"id":"td_001","kind":"open_text","intent":"Length and depth of relationship before founding predicts dynamics under stress.","prompt":"How long have the co-founders known each other, and in what context?","category":"team_dynamics","sensitivity":"standard","feeds_dimensions":["team"],"skip_if_covered_in":["data_room","crm_notes"]},{"id":"td_002","kind":"open_text","intent":"Untested partnerships often crack on first real stress.","prompt":"Have they been through a hard moment together? What happened?","category":"team_dynamics","sensitivity":"standard","feeds_dimensions":["team"]},{"id":"td_003","kind":"open_text","intent":"Ambiguous leadership often shows up later as cap table disputes.","prompt":"Who's clearly the CEO? Is there role clarity, or is it ambiguous?","category":"team_dynamics","sensitivity":"standard","feeds_dimensions":["team"]},{"id":"td_004","kind":"open_text","intent":"Forces a real comparison; the answer often reveals who's actually driving.","prompt":"If you had to bet on one of them solo, who would it be and why?","category":"team_dynamics","sensitivity":"high","feeds_dimensions":["team"]},{"id":"td_005","kind":"open_text","intent":"Tension under low-stakes pitch usually means worse tension under real stress.","prompt":"Did you sense any tension in your meetings — interruptions, contradictions, body language?","category":"team_dynamics","sensitivity":"high","feeds_dimensions":["team"]},{"id":"de_001","kind":"open_text","intent":"Surface the actual edge — not credentials, but proprietary insight.","prompt":"What do they know that others in this space don't?","category":"domain_expertise","sensitivity":"standard","feeds_dimensions":["team","product_technology"]},{"id":"de_002","kind":"open_text","intent":"Concrete advantages, not theoretical ones.","prompt":"What's their unfair access — customers, talent, capital, distribution?","category":"domain_expertise","sensitivity":"standard","feeds_dimensions":["team","competition_moat"]},{"id":"de_003","kind":"open_text","intent":"Operating range matters more than pedigree.","prompt":"Have they shipped before? Hired before? Sold before?","category":"domain_expertise","sensitivity":"standard","feeds_dimensions":["team"],"skip_if_covered_in":["founder_research"]},{"id":"de_004","kind":"open_text","intent":"Identifies founder-specific moat vs. transferable advantages.","prompt":"If a smart competitor entered tomorrow with similar funding, what would these founders have that the competitor wouldn't?","category":"domain_expertise","sensitivity":"standard","feeds_dimensions":["team","competition_moat"]},{"id":"pc_001","kind":"open_text","intent":"Reaction to challenge is one of the most predictive single signals.","prompt":"How did they handle hard questions in your meetings — defensive, curious, honest?","category":"personality_character","sensitivity":"high","feeds_dimensions":["team"]},{"id":"pc_002","kind":"yes_no_with_context","intent":"Self-disclosed failure usually correlates with self-awareness; absence is a flag.","prompt":"Did they tell you about a recent failure unprompted?","category":"personality_character","sensitivity":"high","feeds_dimensions":["team"]},{"id":"pc_003","kind":"open_text","intent":"Predicts ability to attract and keep talent.","prompt":"How do they talk about their team — credit-taking or credit-giving?","category":"personality_character","sensitivity":"high","feeds_dimensions":["team"]},{"id":"pc_004","kind":"open_text","intent":"Dismissiveness toward competitors is a consistent negative signal.","prompt":"How do they talk about competitors — dismissive, respectful, learning-from?","category":"personality_character","sensitivity":"high","feeds_dimensions":["team","competition_moat"]},{"id":"pc_005","kind":"open_text","intent":"Updating in real time is a strong positive; pure defense is concerning.","prompt":"Coachability: did they update on anything you said, or just defend their view?","category":"personality_character","sensitivity":"high","feeds_dimensions":["team"]},{"id":"pc_006","kind":"yes_no_with_context","intent":"Integrity gaps that surface during diligence usually grow, not shrink.","prompt":"Did anything they said turn out not to match what you found later?","category":"personality_character","sensitivity":"high","feeds_dimensions":["team"]},{"id":"pc_007","kind":"open_text","intent":"The single most important question. Trust the answer.","prompt":"What's your gut on whether you'd want to work with this person for 7–10 years through hard things?","category":"personality_character","sensitivity":"high","feeds_dimensions":["team"]},{"id":"pc_008","kind":"open_text","intent":"Pattern-recognition without clear cause is often valid signal. Surface it before it gets rationalized away.","prompt":"Anything that nagged at you that you can't quite articulate?","category":"personality_character","sensitivity":"high","feeds_dimensions":["team"]}],"categories":[{"id":"background_track_record","name":"Background, track record, references","order":1},{"id":"founder_market_fit","name":"Founder-market fit and motivation","order":2},{"id":"team_dynamics","name":"Team dynamics and co-founder relationship","order":3},{"id":"domain_expertise","name":"Domain expertise and unfair advantages","order":4},{"id":"personality_character","name":"Personality and character","order":5}],"skip_logic":{"global_rules":["If CRM interaction notes already answer the question, reference the note and ask a follow-up instead of the original question.","If a prior answer in this session covered the ground, skip and note as covered.","If the data room contains content that answers the question (e.g., founder bio), reference it and ask a verification or follow-up."]},"batching_rules":{"spacing":"Wait for partner answers before sending the next batch. Never send\nall batches at once. If a partner skips more than 2 questions in\na batch, note it and move on rather than pushing.\n","ordering":"Start with background/track record (lower stakes, builds rhythm).\nThen founder-market fit, team dynamics, domain expertise.\nSave personality/character for the last batch — by then the partner\nhas surfaced enough context that those questions land sharper.\n","questions_per_batch":{"max":6,"min":4}},"sensitivity_levels":{"high":"Frame thoughtfully. These questions touch on character, integrity,\nor red flags. Ask in a way that invites honest reflection, not defensiveness.\n","standard":"Ask directly."}},"is_active":true,"edit_note":"Initial defaults from lib/memo-agent/defaults","edited_by":null,"edited_at":"2026-05-11T02:25:23.268743+00:00","created_at":"2026-05-11T02:25:23.268743+00:00"},"data_room_ingestion":{"id":"e73997d3-f33b-4072-a153-e37ec2c93b45","fund_id":"e2dfd2bf-ced3-4647-8277-096e616a6eab","schema_name":"data_room_ingestion","schema_version":"v0.1","yaml_content":"# Memo Agent — Data Room Ingestion Schema\n# Version: 0.1 (draft)\n# Used by: Memo Drafting Agent (Stage 1 — ingestion)\n#\n# Defines: how each document in a Google Drive data room gets parsed\n# into structured claims, what document types we recognize, what we\n# expect to find (so we can flag gaps), and how financial models get\n# special treatment so company projections don't leak in as facts.\n\nmeta:\n  version: \"0.1\"\n  last_updated: \"2026-05-06\"\n  owner: vendor   # Technical schema; partners review but don't typically edit.\n\n# ---------------------------------------------------------------------------\n# Provenance — every claim from the data room carries this by default\n# ---------------------------------------------------------------------------\n\nprovenance_default:\n  source_type: company_stated\n  rationale: |\n    Every claim extracted from the data room is treated as\n    \"company-stated\" — the company has asserted it; it has NOT been\n    independently verified. The agent must never elevate a\n    company-stated claim to a fact in the memo. Verification belongs\n    to Stage 2 (external research) and Stage 3 (partner Q&A). This\n    is the single most important guardrail in this schema.\n\n# ---------------------------------------------------------------------------\n# Document types\n# ---------------------------------------------------------------------------\n\ndocument_types:\n  - id: pitch_deck\n    name: \"Pitch deck\"\n    typical_formats: [pdf, pptx, gslides]\n    typical_content: [problem, solution, market, traction, team, ask]\n    extraction_priority: high\n\n  - id: financial_model\n    name: \"Financial model / projections\"\n    typical_formats: [xlsx, gsheets]\n    typical_content: [historical_metrics, projections, assumptions, unit_economics]\n    extraction_priority: high\n    special_handling: financial_extraction   # see below\n\n  - id: cap_table\n    name: \"Cap table\"\n    typical_formats: [xlsx, gsheets, pdf]\n    typical_content: [ownership, option_pool, prior_rounds, preferences]\n    extraction_priority: high\n\n  - id: founder_bios\n    name: \"Founder bios / resumes\"\n    typical_formats: [pdf, docx]\n    typical_content: [education, prior_roles, exits, publications]\n    extraction_priority: high\n\n  - id: customer_reference\n    name: \"Customer reference / case study\"\n    typical_formats: [pdf, docx, gdocs, video, audio]\n    typical_content: [customer_name, use_case, outcomes, willingness_to_reference]\n    extraction_priority: high\n\n  - id: product_demo\n    name: \"Product demo\"\n    typical_formats: [video, mp4, mov]\n    typical_content: [product_walkthrough, ux, capabilities]\n    extraction_priority: medium\n    note: \"Transcribe audio; extract feature list and key claims.\"\n\n  - id: technical_doc\n    name: \"Technical architecture / spec\"\n    typical_formats: [pdf, docx, gdocs, md]\n    typical_content: [architecture, stack, ip, moat_claims]\n    extraction_priority: medium\n\n  - id: market_analysis_internal\n    name: \"Company-prepared market analysis\"\n    typical_formats: [pdf, docx, gdocs, pptx]\n    typical_content: [tam_sam_som, competitors, trends]\n    extraction_priority: medium\n    note: |\n      Treat with extra skepticism. Company-prepared analyses tend to be\n      selectively sourced. Flag every claim for verification in Stage 2.\n\n  - id: prior_round_terms\n    name: \"Prior round terms / SAFE / agreements\"\n    typical_formats: [pdf, docx]\n    typical_content: [valuation, investors, rights, preferences]\n    extraction_priority: high\n\n  - id: hiring_plan\n    name: \"Hiring plan\"\n    typical_formats: [xlsx, gsheets, pdf]\n    typical_content: [roles, timing, comp_bands]\n    extraction_priority: medium\n\n  - id: roadmap\n    name: \"Product roadmap\"\n    typical_formats: [pdf, pptx, gsheets]\n    typical_content: [milestones, timing, features]\n    extraction_priority: medium\n\n  - id: whiteboard_or_sketch\n    name: \"Whiteboard photo / strategy sketch\"\n    typical_formats: [jpg, png, heic]\n    typical_content: [informal_diagrams, strategy_notes]\n    extraction_priority: low\n    note: \"OCR plus visual interpretation. Often partial; flag low confidence.\"\n\n  - id: press_coverage\n    name: \"Press coverage / clips\"\n    typical_formats: [pdf, link, gdocs]\n    typical_content: [headlines, publication, date, tone]\n    extraction_priority: low\n    note: \"Useful as signal, not as fact.\"\n\n  - id: other\n    name: \"Other / unclassified\"\n    typical_formats: [any]\n    extraction_priority: low\n    note: \"Catch-all. Surface for partner review rather than guessing.\"\n\n# ---------------------------------------------------------------------------\n# Expected documents — drives gap analysis\n# ---------------------------------------------------------------------------\n\nexpected_documents:\n  description: |\n    What we'd typically expect to find in a complete data room. Items\n    not present become entries in the gap_analysis output. Absence\n    isn't necessarily a flag — but it should always be visible.\n  expected:\n    - { id: pitch_deck,         criticality: required }\n    - { id: financial_model,    criticality: required }\n    - { id: cap_table,          criticality: required }\n    - { id: founder_bios,       criticality: required }\n    - { id: customer_reference, criticality: expected }\n    - { id: product_demo,       criticality: expected }\n    - { id: prior_round_terms,  criticality: expected }\n    - { id: hiring_plan,        criticality: optional }\n    - { id: roadmap,            criticality: optional }\n\n# ---------------------------------------------------------------------------\n# Per-document extraction format\n# ---------------------------------------------------------------------------\n\ndocument_record:\n  fields:\n    - { name: id,                  type: string,   required: true,  note: \"Stable per-data-room ID.\" }\n    - { name: file_name,           type: string,   required: true }\n    - { name: file_format,         type: string,   required: true,  note: \"pdf, xlsx, gslides, mp4, etc.\" }\n    - { name: drive_path,          type: string,   required: true }\n    - { name: drive_file_id,       type: string,   required: true,  note: \"Google Drive file ID for traceability.\" }\n    - { name: detected_type,       type: string,   required: true,  note: \"ID from document_types.\" }\n    - { name: type_confidence,     type: enum,     required: true,  values: [low, medium, high] }\n    - { name: document_date,       type: date,     required: false, note: \"Author date if found; else file modified date.\" }\n    - { name: page_or_slide_count, type: integer,  required: false }\n    - { name: parse_status,        type: enum,     required: true,  values: [parsed, partial, failed, skipped] }\n    - { name: parse_notes,         type: string,   required: false, note: \"Why partial/failed (scanned, password-protected, etc.).\" }\n    - { name: summary,             type: string,   required: true,  note: \"1–3 sentence summary of the document's purpose and content.\" }\n    - { name: claims,              type: list,     required: false, note: \"List of claim_record entries — see below.\" }\n    - { name: financial_extract,   type: object,   required: false, note: \"Only for financial_model; see financial_extraction.\" }\n\n# ---------------------------------------------------------------------------\n# Claim record — every meaningful assertion extracted from the data room\n# ---------------------------------------------------------------------------\n\nclaim_record:\n  description: |\n    A discrete assertion extracted from a document. Each claim is\n    tagged with provenance, the section of the memo it likely feeds,\n    and a verification status the research stage will update.\n  fields:\n    - { name: id,                  type: string,  required: true,  note: \"Stable claim ID, e.g. 'pitch_deck_001_claim_03'.\" }\n    - { name: text,                type: string,  required: true,  note: \"The claim, phrased neutrally — strip marketing voice.\" }\n    - { name: claim_type,          type: enum,    required: true,  values: [metric, market_size, competitive_position, customer_evidence, team_credential, product_capability, financial_projection, other] }\n    - { name: source_document_id,  type: string,  required: true }\n    - { name: source_location,     type: string,  required: false, note: \"Page, slide, cell — wherever the claim lives.\" }\n    - { name: source_type,         type: enum,    required: true,  values: [company_stated, third_party_cited_by_company, customer_quoted_by_company] }\n    - { name: feeds_memo_section,  type: list,    required: true,  note: \"Memo sections this claim is likely relevant to.\" }\n    - { name: verification_status, type: enum,    required: true,  values: [unverified, verification_attempted, verified, contradicted, unverifiable] }\n    - { name: verification_notes,  type: string,  required: false, note: \"How and where it was verified, or why not.\" }\n\n# ---------------------------------------------------------------------------\n# Financial model — special handling\n# ---------------------------------------------------------------------------\n\nfinancial_extraction:\n  rationale: |\n    Financial models are dense with numbers that look like facts. They\n    are not. Every projection rests on assumptions, and the assumptions\n    are where the actual judgment lives. The agent extracts both, links\n    each metric to its driving assumptions, and surfaces aggressiveness\n    versus historical actuals where comparison is possible. The agent\n    does NOT critique the model — it makes the assumption layer visible\n    so the partner can.\n\n  metrics_to_extract:\n    - { id: arr,             name: \"ARR\",                   units: currency, historical: required, projected: required }\n    - { id: arr_growth,      name: \"ARR growth rate\",       units: percent,  historical: required, projected: required }\n    - { id: gross_margin,    name: \"Gross margin\",          units: percent,  historical: required, projected: required }\n    - { id: net_margin,      name: \"Net margin\",            units: percent,  historical: optional, projected: optional }\n    - { id: burn,            name: \"Net burn (monthly)\",    units: currency, historical: required, projected: required }\n    - { id: runway,          name: \"Runway (months)\",       units: months,   historical: required, projected: required }\n    - { id: cac,             name: \"CAC\",                   units: currency, historical: optional, projected: optional }\n    - { id: ltv,             name: \"LTV\",                   units: currency, historical: optional, projected: optional }\n    - { id: ltv_cac,         name: \"LTV:CAC ratio\",         units: ratio,    historical: optional, projected: optional }\n    - { id: payback_months,  name: \"CAC payback (months)\",  units: months,   historical: optional, projected: optional }\n    - { id: nrr,             name: \"Net revenue retention\", units: percent,  historical: optional, projected: optional }\n    - { id: gross_retention, name: \"Gross retention\",       units: percent,  historical: optional, projected: optional }\n    - { id: headcount,       name: \"Headcount\",             units: count,    historical: required, projected: required }\n\n  metric_record:\n    fields:\n      - { name: metric_id,           type: string }\n      - { name: value,               type: number_or_range }\n      - { name: period,              type: string,   note: \"e.g., 'FY2025', 'Q3 2025', 'projected EOY 2027'.\" }\n      - { name: is_projection,       type: boolean }\n      - { name: cell_reference,      type: string,   note: \"Sheet name + cell, for traceability.\" }\n      - { name: driving_assumptions, type: list,     note: \"List of assumption_record IDs this metric depends on.\" }\n\n  assumption_record:\n    fields:\n      - { name: id,                     type: string }\n      - { name: text,                   type: string,  note: \"The assumption, stated plainly. e.g., 'Sales rep ramps to full quota in 4 months.'\" }\n      - { name: cell_reference,         type: string }\n      - { name: assumption_type,        type: enum,    values: [growth_rate, conversion_rate, churn_rate, headcount_plan, pricing, cogs, opex_ratio, other] }\n      - { name: aggressiveness,         type: enum,    values: [conservative, in_line_with_history, optimistic, aggressive, unmarked], note: \"Compared to historical actuals where available; 'unmarked' when no historical comparison possible.\" }\n      - { name: partner_attention_flag, type: boolean, note: \"True when the assumption is materially more aggressive than historical actuals.\" }\n\n  rules:\n    - \"Never quote a projected number without surfacing its driving assumptions in the memo or appendix.\"\n    - \"Mark every projection as 'projected' in the memo; do not blur the historical/projected line.\"\n    - \"Flag assumptions materially more aggressive than historical actuals; do not editorialize on whether they are achievable.\"\n    - \"The agent does not score the model. It surfaces structure for partner judgment.\"\n\n# ---------------------------------------------------------------------------\n# Gap analysis — what's expected but missing\n# ---------------------------------------------------------------------------\n\ngap_analysis:\n  description: |\n    Run after all documents are ingested. Compare detected types\n    against `expected_documents`. Output the list of expected items\n    that were not found, with criticality and implication.\n  output_record:\n    fields:\n      - { name: missing_type,            type: string }\n      - { name: criticality,             type: enum,   values: [required, expected, optional] }\n      - { name: implication,             type: string, note: \"What this absence means for the memo (e.g., 'Without a financial model, projections section will be partner-driven only.').\" }\n      - { name: partner_recommendation,  type: string, note: \"What the agent recommends asking the company for.\" }\n\n# ---------------------------------------------------------------------------\n# Ingestion output — the artifact handed to Stage 2 (research) and Stage 4 (drafting)\n# ---------------------------------------------------------------------------\n\ningestion_output:\n  fields:\n    - { name: data_room_id,         type: string,    required: true }\n    - { name: ingestion_date,       type: datetime,  required: true }\n    - { name: documents,            type: list,      required: true,  note: \"List of document_record entries.\" }\n    - { name: claims,               type: list,      required: true,  note: \"Flattened list of all claim_record entries across documents.\" }\n    - { name: financial_summary,    type: object,    required: false, note: \"Aggregated financial_extraction across the model(s).\" }\n    - { name: gaps,                 type: list,      required: true,  note: \"List of gap_analysis entries.\" }\n    - { name: partner_review_notes, type: string,    required: false, note: \"Free text for the agent's overall observations — what surprised it, what's missing, what looked unusual.\" }\n","parsed_content":{"meta":{"owner":"vendor","version":"0.1","last_updated":"2026-05-06"},"claim_record":{"fields":[{"name":"id","note":"Stable claim ID, e.g. 'pitch_deck_001_claim_03'.","type":"string","required":true},{"name":"text","note":"The claim, phrased neutrally — strip marketing voice.","type":"string","required":true},{"name":"claim_type","type":"enum","values":["metric","market_size","competitive_position","customer_evidence","team_credential","product_capability","financial_projection","other"],"required":true},{"name":"source_document_id","type":"string","required":true},{"name":"source_location","note":"Page, slide, cell — wherever the claim lives.","type":"string","required":false},{"name":"source_type","type":"enum","values":["company_stated","third_party_cited_by_company","customer_quoted_by_company"],"required":true},{"name":"feeds_memo_section","note":"Memo sections this claim is likely relevant to.","type":"list","required":true},{"name":"verification_status","type":"enum","values":["unverified","verification_attempted","verified","contradicted","unverifiable"],"required":true},{"name":"verification_notes","note":"How and where it was verified, or why not.","type":"string","required":false}],"description":"A discrete assertion extracted from a document. Each claim is\ntagged with provenance, the section of the memo it likely feeds,\nand a verification status the research stage will update.\n"},"gap_analysis":{"description":"Run after all documents are ingested. Compare detected types\nagainst `expected_documents`. Output the list of expected items\nthat were not found, with criticality and implication.\n","output_record":{"fields":[{"name":"missing_type","type":"string"},{"name":"criticality","type":"enum","values":["required","expected","optional"]},{"name":"implication","note":"What this absence means for the memo (e.g., 'Without a financial model, projections section will be partner-driven only.').","type":"string"},{"name":"partner_recommendation","note":"What the agent recommends asking the company for.","type":"string"}]}},"document_types":[{"id":"pitch_deck","name":"Pitch deck","typical_content":["problem","solution","market","traction","team","ask"],"typical_formats":["pdf","pptx","gslides"],"extraction_priority":"high"},{"id":"financial_model","name":"Financial model / projections","typical_content":["historical_metrics","projections","assumptions","unit_economics"],"typical_formats":["xlsx","gsheets"],"special_handling":"financial_extraction","extraction_priority":"high"},{"id":"cap_table","name":"Cap table","typical_content":["ownership","option_pool","prior_rounds","preferences"],"typical_formats":["xlsx","gsheets","pdf"],"extraction_priority":"high"},{"id":"founder_bios","name":"Founder bios / resumes","typical_content":["education","prior_roles","exits","publications"],"typical_formats":["pdf","docx"],"extraction_priority":"high"},{"id":"customer_reference","name":"Customer reference / case study","typical_content":["customer_name","use_case","outcomes","willingness_to_reference"],"typical_formats":["pdf","docx","gdocs","video","audio"],"extraction_priority":"high"},{"id":"product_demo","name":"Product demo","note":"Transcribe audio; extract feature list and key claims.","typical_content":["product_walkthrough","ux","capabilities"],"typical_formats":["video","mp4","mov"],"extraction_priority":"medium"},{"id":"technical_doc","name":"Technical architecture / spec","typical_content":["architecture","stack","ip","moat_claims"],"typical_formats":["pdf","docx","gdocs","md"],"extraction_priority":"medium"},{"id":"market_analysis_internal","name":"Company-prepared market analysis","note":"Treat with extra skepticism. Company-prepared analyses tend to be\nselectively sourced. Flag every claim for verification in Stage 2.\n","typical_content":["tam_sam_som","competitors","trends"],"typical_formats":["pdf","docx","gdocs","pptx"],"extraction_priority":"medium"},{"id":"prior_round_terms","name":"Prior round terms / SAFE / agreements","typical_content":["valuation","investors","rights","preferences"],"typical_formats":["pdf","docx"],"extraction_priority":"high"},{"id":"hiring_plan","name":"Hiring plan","typical_content":["roles","timing","comp_bands"],"typical_formats":["xlsx","gsheets","pdf"],"extraction_priority":"medium"},{"id":"roadmap","name":"Product roadmap","typical_content":["milestones","timing","features"],"typical_formats":["pdf","pptx","gsheets"],"extraction_priority":"medium"},{"id":"whiteboard_or_sketch","name":"Whiteboard photo / strategy sketch","note":"OCR plus visual interpretation. Often partial; flag low confidence.","typical_content":["informal_diagrams","strategy_notes"],"typical_formats":["jpg","png","heic"],"extraction_priority":"low"},{"id":"press_coverage","name":"Press coverage / clips","note":"Useful as signal, not as fact.","typical_content":["headlines","publication","date","tone"],"typical_formats":["pdf","link","gdocs"],"extraction_priority":"low"},{"id":"other","name":"Other / unclassified","note":"Catch-all. Surface for partner review rather than guessing.","typical_formats":["any"],"extraction_priority":"low"}],"document_record":{"fields":[{"name":"id","note":"Stable per-data-room ID.","type":"string","required":true},{"name":"file_name","type":"string","required":true},{"name":"file_format","note":"pdf, xlsx, gslides, mp4, etc.","type":"string","required":true},{"name":"drive_path","type":"string","required":true},{"name":"drive_file_id","note":"Google Drive file ID for traceability.","type":"string","required":true},{"name":"detected_type","note":"ID from document_types.","type":"string","required":true},{"name":"type_confidence","type":"enum","values":["low","medium","high"],"required":true},{"name":"document_date","note":"Author date if found; else file modified date.","type":"date","required":false},{"name":"page_or_slide_count","type":"integer","required":false},{"name":"parse_status","type":"enum","values":["parsed","partial","failed","skipped"],"required":true},{"name":"parse_notes","note":"Why partial/failed (scanned, password-protected, etc.).","type":"string","required":false},{"name":"summary","note":"1–3 sentence summary of the document's purpose and content.","type":"string","required":true},{"name":"claims","note":"List of claim_record entries — see below.","type":"list","required":false},{"name":"financial_extract","note":"Only for financial_model; see financial_extraction.","type":"object","required":false}]},"ingestion_output":{"fields":[{"name":"data_room_id","type":"string","required":true},{"name":"ingestion_date","type":"datetime","required":true},{"name":"documents","note":"List of document_record entries.","type":"list","required":true},{"name":"claims","note":"Flattened list of all claim_record entries across documents.","type":"list","required":true},{"name":"financial_summary","note":"Aggregated financial_extraction across the model(s).","type":"object","required":false},{"name":"gaps","note":"List of gap_analysis entries.","type":"list","required":true},{"name":"partner_review_notes","note":"Free text for the agent's overall observations — what surprised it, what's missing, what looked unusual.","type":"string","required":false}]},"expected_documents":{"expected":[{"id":"pitch_deck","criticality":"required"},{"id":"financial_model","criticality":"required"},{"id":"cap_table","criticality":"required"},{"id":"founder_bios","criticality":"required"},{"id":"customer_reference","criticality":"expected"},{"id":"product_demo","criticality":"expected"},{"id":"prior_round_terms","criticality":"expected"},{"id":"hiring_plan","criticality":"optional"},{"id":"roadmap","criticality":"optional"}],"description":"What we'd typically expect to find in a complete data room. Items\nnot present become entries in the gap_analysis output. Absence\nisn't necessarily a flag — but it should always be visible.\n"},"provenance_default":{"rationale":"Every claim extracted from the data room is treated as\n\"company-stated\" — the company has asserted it; it has NOT been\nindependently verified. The agent must never elevate a\ncompany-stated claim to a fact in the memo. Verification belongs\nto Stage 2 (external research) and Stage 3 (partner Q&A). This\nis the single most important guardrail in this schema.\n","source_type":"company_stated"},"financial_extraction":{"rules":["Never quote a projected number without surfacing its driving assumptions in the memo or appendix.","Mark every projection as 'projected' in the memo; do not blur the historical/projected line.","Flag assumptions materially more aggressive than historical actuals; do not editorialize on whether they are achievable.","The agent does not score the model. It surfaces structure for partner judgment."],"rationale":"Financial models are dense with numbers that look like facts. They\nare not. Every projection rests on assumptions, and the assumptions\nare where the actual judgment lives. The agent extracts both, links\neach metric to its driving assumptions, and surfaces aggressiveness\nversus historical actuals where comparison is possible. The agent\ndoes NOT critique the model — it makes the assumption layer visible\nso the partner can.\n","metric_record":{"fields":[{"name":"metric_id","type":"string"},{"name":"value","type":"number_or_range"},{"name":"period","note":"e.g., 'FY2025', 'Q3 2025', 'projected EOY 2027'.","type":"string"},{"name":"is_projection","type":"boolean"},{"name":"cell_reference","note":"Sheet name + cell, for traceability.","type":"string"},{"name":"driving_assumptions","note":"List of assumption_record IDs this metric depends on.","type":"list"}]},"assumption_record":{"fields":[{"name":"id","type":"string"},{"name":"text","note":"The assumption, stated plainly. e.g., 'Sales rep ramps to full quota in 4 months.'","type":"string"},{"name":"cell_reference","type":"string"},{"name":"assumption_type","type":"enum","values":["growth_rate","conversion_rate","churn_rate","headcount_plan","pricing","cogs","opex_ratio","other"]},{"name":"aggressiveness","note":"Compared to historical actuals where available; 'unmarked' when no historical comparison possible.","type":"enum","values":["conservative","in_line_with_history","optimistic","aggressive","unmarked"]},{"name":"partner_attention_flag","note":"True when the assumption is materially more aggressive than historical actuals.","type":"boolean"}]},"metrics_to_extract":[{"id":"arr","name":"ARR","units":"currency","projected":"required","historical":"required"},{"id":"arr_growth","name":"ARR growth rate","units":"percent","projected":"required","historical":"required"},{"id":"gross_margin","name":"Gross margin","units":"percent","projected":"required","historical":"required"},{"id":"net_margin","name":"Net margin","units":"percent","projected":"optional","historical":"optional"},{"id":"burn","name":"Net burn (monthly)","units":"currency","projected":"required","historical":"required"},{"id":"runway","name":"Runway (months)","units":"months","projected":"required","historical":"required"},{"id":"cac","name":"CAC","units":"currency","projected":"optional","historical":"optional"},{"id":"ltv","name":"LTV","units":"currency","projected":"optional","historical":"optional"},{"id":"ltv_cac","name":"LTV:CAC ratio","units":"ratio","projected":"optional","historical":"optional"},{"id":"payback_months","name":"CAC payback (months)","units":"months","projected":"optional","historical":"optional"},{"id":"nrr","name":"Net revenue retention","units":"percent","projected":"optional","historical":"optional"},{"id":"gross_retention","name":"Gross retention","units":"percent","projected":"optional","historical":"optional"},{"id":"headcount","name":"Headcount","units":"count","projected":"required","historical":"required"}]}},"is_active":true,"edit_note":"Initial defaults from lib/memo-agent/defaults","edited_by":null,"edited_at":"2026-05-11T02:25:23.399687+00:00","created_at":"2026-05-11T02:25:23.399687+00:00"},"research_dossier":{"id":"967b52e3-da41-45d0-b65a-e34f185b837b","fund_id":"e2dfd2bf-ced3-4647-8277-096e616a6eab","schema_name":"research_dossier","schema_version":"v0.1","yaml_content":"# Memo Agent — External Research Dossier Schema\n# Version: 0.1 (draft)\n# Used by: Memo Drafting Agent (Stage 2 — external research)\n#\n# Symmetric partner to data_room_ingestion.yaml. Where that captures\n# what the COMPANY says, this captures what the WORLD says — with\n# the same provenance / confidence / feeds-section discipline.\n#\n# Findings link back to claims in the ingestion output by claim ID,\n# updating verification_status. The two schemas together fully define\n# the input to memo drafting (Stage 4).\n\nmeta:\n  version: \"0.1\"\n  last_updated: \"2026-05-06\"\n  owner: vendor\n  notes: |\n    Per spec constraint: NO LinkedIn scraping. Founder research draws\n    from the alternative source taxonomy below. Per spec policy: the\n    agent compiles research material on founders but does NOT score\n    the team dimension — that remains partner-only (see rubric.yaml).\n\n# ---------------------------------------------------------------------------\n# Categories of research\n# ---------------------------------------------------------------------------\n\nresearch_categories:\n  - id: market\n    name: \"Market validation\"\n    description: \"Independent verification of market size, growth, and dynamics claimed by the company.\"\n    feeds_dimensions: [market]\n\n  - id: competitive\n    name: \"Competitive landscape\"\n    description: \"Direct, adjacent, and incumbent competitors. Includes competitors the company did not mention — usually the most valuable output of this category.\"\n    feeds_dimensions: [competition_moat, product_technology]\n\n  - id: founder_team\n    name: \"Founder and team research\"\n    description: \"Background, prior work, public statements, technical output. Compiled for partner judgment; never scored by the agent.\"\n    feeds_dimensions: [team]   # Partner-only dimension. Agent surfaces material; partner scores.\n\n  - id: customer_validation\n    name: \"Customer and demand validation\"\n    description: \"Independent signals of customer demand or product reception — reviews, forums, public references.\"\n    feeds_dimensions: [traction, product_technology]\n\n  - id: regulatory\n    name: \"Regulatory and policy environment\"\n    description: \"Tailwinds, headwinds, pending legislation, compliance requirements.\"\n    feeds_dimensions: [market, business_model]\n\n  - id: technical_validation\n    name: \"Technical validation\"\n    description: \"Independent assessment of technical claims — papers, benchmarks, engineering output.\"\n    feeds_dimensions: [product_technology, competition_moat]\n\n# ---------------------------------------------------------------------------\n# Source types and quality tiers\n# ---------------------------------------------------------------------------\n# Source quality drives confidence. The agent must record source type\n# alongside every finding. \"I read it on the internet\" is not a finding.\n\nsource_quality_tiers:\n  tier_1_authoritative:\n    description: \"Primary or near-primary sources with strong editorial or methodological standards.\"\n    examples:\n      - \"Government data (Census, BLS, SEC filings, regulatory filings)\"\n      - \"Peer-reviewed research\"\n      - \"Major analyst firms with disclosed methodology (Gartner, Forrester, IDC)\"\n      - \"Industry associations with published methodology\"\n      - \"Court records, patent filings\"\n    default_confidence: high\n\n  tier_2_credible:\n    description: \"Established journalism and research with editorial standards but secondary.\"\n    examples:\n      - \"Established business journalism (FT, WSJ, Bloomberg, Reuters, The Information)\"\n      - \"Vertical trade press with reputation\"\n      - \"Crunchbase, PitchBook (for funding/round data)\"\n      - \"Company press releases and SEC filings (for that company's own facts)\"\n    default_confidence: medium_to_high\n\n  tier_3_signal:\n    description: \"Useful as signal of sentiment or surface-level fact; not relied on alone for material claims.\"\n    examples:\n      - \"Company website and marketing materials (third-party companies — not the target company)\"\n      - \"Podcast interviews, conference talks\"\n      - \"Founder personal blogs, Substacks\"\n      - \"Product Hunt, G2, Capterra reviews (signal, not statistic)\"\n      - \"GitHub repositories and release history\"\n    default_confidence: medium\n\n  tier_4_weak:\n    description: \"Use only with corroboration. Never the sole source for a material claim.\"\n    examples:\n      - \"Reddit, Hacker News, Twitter/X threads\"\n      - \"Glassdoor reviews\"\n      - \"Anonymous forum posts\"\n      - \"Aggregator and SEO-optimized listicles\"\n    default_confidence: low\n\n  tier_excluded:\n    description: \"Never used.\"\n    examples:\n      - \"LinkedIn scraping (per spec)\"\n      - \"Sites that require authentication or bypass paywalls\"\n      - \"Dark social, leaked private data\"\n      - \"Sites flagged for misinformation or extremism\"\n\n# ---------------------------------------------------------------------------\n# Finding record — the unit of external research\n# ---------------------------------------------------------------------------\n\nfinding_record:\n  description: \"A discrete piece of external information the agent has gathered.\"\n  fields:\n    - { name: id,                        type: string,  required: true,  note: \"Stable finding ID, e.g. 'finding_market_001'.\" }\n    - { name: category,                  type: string,  required: true,  note: \"ID from research_categories.\" }\n    - { name: text,                      type: string,  required: true,  note: \"The finding, phrased neutrally.\" }\n    - { name: finding_type,              type: enum,    required: true,  values: [fact, signal, opinion, datapoint, contradiction], note: \"fact = independently established; signal = directional but not conclusive; opinion = third-party interpretation; datapoint = a specific number; contradiction = directly contradicts a company claim.\" }\n    - { name: source_url,                type: url,     required: true,  note: \"Direct URL. If multiple sources, the primary.\" }\n    - { name: source_publisher,          type: string,  required: true,  note: \"e.g., 'Bloomberg', 'SEC EDGAR', 'GitHub'.\" }\n    - { name: source_date,               type: date,    required: false, note: \"Publication date. Recency matters; flag if >18 months old.\" }\n    - { name: source_quality_tier,       type: enum,    required: true,  values: [tier_1_authoritative, tier_2_credible, tier_3_signal, tier_4_weak] }\n    - { name: corroborating_sources,     type: list,    required: false, note: \"Additional URLs supporting the same finding. Multi-source raises confidence.\" }\n    - { name: confidence,                type: enum,    required: true,  values: [low, medium, high] }\n    - { name: confidence_rationale,      type: string,  required: false, note: \"Why this confidence level — source tier, corroboration, recency, methodology.\" }\n    - { name: feeds_memo_section,        type: list,    required: true,  note: \"Memo sections this finding is relevant to.\" }\n    - { name: relates_to_claim_id,       type: string,  required: false, note: \"If this finding bears on a specific data-room claim, reference it here.\" }\n    - { name: relationship_to_claim,     type: enum,    required: false, values: [confirms, partially_confirms, contradicts, contextualizes, unrelated], note: \"How this finding relates to the company-stated claim.\" }\n\n# ---------------------------------------------------------------------------\n# Contradiction record — surfaced separately because they matter most\n# ---------------------------------------------------------------------------\n\ncontradiction_record:\n  rationale: |\n    When external evidence contradicts a company-stated claim, that's\n    the highest-value output of the research stage. Partners need\n    these surfaced clearly, not buried in a flat list of findings.\n    Every contradiction also writes back to the originating claim's\n    verification_status (set to 'contradicted' in the ingestion\n    output).\n  fields:\n    - { name: id,                  type: string,  required: true }\n    - { name: company_claim_id,    type: string,  required: true,  note: \"Claim ID from data_room_ingestion.\" }\n    - { name: company_claim_text,  type: string,  required: true,  note: \"Restated for partner readability.\" }\n    - { name: contradicting_finding_ids, type: list, required: true }\n    - { name: nature,              type: enum,    required: true,  values: [factual, definitional, methodological, temporal, scope], note: \"factual = numbers don't match; definitional = different definition of the same term; methodological = different measurement approach; temporal = the claim was true at one time but isn't now; scope = the claim is true for a subset but stated as universal.\" }\n    - { name: severity,            type: enum,    required: true,  values: [minor, moderate, material], note: \"How much it should affect partner judgment.\" }\n    - { name: agent_notes,         type: string,  required: false, note: \"Neutral framing of the discrepancy. Do not editorialize on intent.\" }\n\n# ---------------------------------------------------------------------------\n# Founder & team research — special handling\n# ---------------------------------------------------------------------------\n\nfounder_research_special_handling:\n  rationale: |\n    Constraints: no LinkedIn scraping; team dimension is partner-only;\n    the agent compiles material but does not interpret character. This\n    section defines what's in-scope, what's out-of-scope, and the\n    alternative source taxonomy that fills the gap LinkedIn would\n    otherwise occupy.\n\n  in_scope:\n    - \"Public professional history from company websites, press, official bios\"\n    - \"Published work: papers, patents, talks, books, podcasts\"\n    - \"Open source contributions (GitHub, package registries)\"\n    - \"Public statements: interviews, panel appearances, public letters\"\n    - \"Verifiable affiliations: prior companies, advisor roles, board seats\"\n    - \"Press mentions including coverage of prior ventures, exits, lawsuits\"\n    - \"Court records (subject to ethical and legal considerations)\"\n    - \"Educational credentials where publicly verified\"\n\n  out_of_scope:\n    - \"LinkedIn scraping (per spec constraint)\"\n    - \"Personal social media used for personal life (non-professional content)\"\n    - \"Speculation about personal relationships, family, or health\"\n    - \"Inference from photos, lifestyle, or appearance\"\n    - \"Anonymous accusations without corroboration\"\n    - \"Any character interpretation — the agent surfaces material; the partner interprets\"\n\n  alternative_source_taxonomy:\n    primary_sources:\n      - { id: company_history,       name: \"Prior company websites and About pages\", priority: high }\n      - { id: press_coverage,        name: \"Press coverage of prior roles and exits\", priority: high }\n      - { id: government_filings,    name: \"SEC, Companies House, regulatory filings\", priority: high, note: \"For director histories and prior ventures.\" }\n      - { id: court_records,         name: \"Public court records\",                     priority: medium, note: \"Use cautiously; pending matters are not findings.\" }\n      - { id: patent_filings,        name: \"USPTO, EPO, WIPO\",                         priority: medium }\n      - { id: academic_publications, name: \"Google Scholar, arXiv, peer-reviewed work\", priority: high, note: \"For technical founders.\" }\n      - { id: open_source,           name: \"GitHub, package registries\",                priority: high, note: \"For technical founders — code is the strongest available signal.\" }\n      - { id: conference_talks,      name: \"YouTube talks, conference recordings\",      priority: medium }\n      - { id: podcast_interviews,    name: \"Podcast appearances\",                       priority: medium, note: \"Often more candid than written press.\" }\n      - { id: long_form_interviews,  name: \"Magazine profiles, deep-dive articles\",     priority: medium }\n      - { id: founder_writing,       name: \"Personal blogs, Substacks, op-eds\",         priority: medium, note: \"Distinguish between personal voice and ghostwritten content.\" }\n\n    backchannel_signals:\n      description: \"Your CRM's connection mapping surfaces who in the partner network has worked with these founders. The agent identifies the connections; the partner does the actual outreach. Backchannel content is never reported in the dossier as a finding — it is collected by the partner and entered through Q&A.\"\n      agent_action: \"Surface the connection list with prior affiliations. Do not contact, do not summarize what others might say.\"\n\n  output_constraints:\n    - \"Compile material. Do not interpret character.\"\n    - \"Direct quotes only when source is reliable; paraphrase otherwise.\"\n    - \"Flag inconsistencies between data room founder bios and external sources.\"\n    - \"Flag prior ventures the company did not mention.\"\n    - \"Never state or imply a personal characteristic that isn't directly evidenced.\"\n\n# ---------------------------------------------------------------------------\n# Competitive research — special handling\n# ---------------------------------------------------------------------------\n\ncompetitive_research_special_handling:\n  rationale: |\n    The most valuable output of competitive research is usually the\n    competitors the company did NOT mention. Companies systematically\n    under-represent competition in pitch materials. This section\n    forces the agent to look beyond the company-supplied list.\n\n  required_outputs:\n    - { id: company_named_competitors,    name: \"Competitors named by the company\", required: true }\n    - { id: company_unnamed_competitors,  name: \"Competitors NOT named by the company but operating in the same space\", required: true, note: \"Highest-value output of this stage.\" }\n    - { id: adjacent_competitors,         name: \"Adjacent players who could enter\", required: true }\n    - { id: incumbent_competitors,        name: \"Incumbents who already serve this need (often with worse products)\", required: true }\n    - { id: build_vs_buy_alternatives,    name: \"What customers do today instead of buying anything\", required: false, note: \"Sometimes 'a spreadsheet' is the real competitor.\" }\n\n  per_competitor_record:\n    fields:\n      - { name: id,                    type: string }\n      - { name: name,                  type: string }\n      - { name: category,              type: enum,    values: [direct, adjacent, incumbent, alternative] }\n      - { name: company_mentioned,     type: boolean, note: \"Did the company name them in the data room?\" }\n      - { name: positioning_summary,   type: string,  note: \"1–2 sentence neutral description of what they do.\" }\n      - { name: stage,                 type: enum,    values: [seed, series_a, series_b, growth, public, private_mature, unknown] }\n      - { name: most_recent_funding,   type: object,  required: false, note: \"Round, amount, date, lead — when known.\" }\n      - { name: differentiation_vs_target, type: string, note: \"How target company is positioned differently. Neutral framing.\" }\n      - { name: source_urls,           type: list }\n\n# ---------------------------------------------------------------------------\n# Research output — handed to Stage 4 (drafting)\n# ---------------------------------------------------------------------------\n\nresearch_output:\n  fields:\n    - { name: dossier_id,        type: string,    required: true }\n    - { name: research_date,     type: datetime,  required: true }\n    - { name: findings,          type: list,      required: true,  note: \"All finding_record entries.\" }\n    - { name: contradictions,    type: list,      required: true,  note: \"All contradiction_record entries.\" }\n    - { name: competitive_map,   type: object,    required: true,  note: \"Output of competitive_research_special_handling.\" }\n    - { name: founder_dossiers,  type: list,      required: true,  note: \"One per founder; compiled material only.\" }\n    - { name: claim_verifications, type: list,    required: true,  note: \"Updates to data_room_ingestion claim verification_status fields.\" }\n    - { name: research_gaps,     type: list,      required: false, note: \"Areas where the agent could not find independent sourcing — surfaced for partner attention.\" }\n    - { name: agent_notes,       type: string,    required: false, note: \"Neutral overall observations: what was easy to verify, what wasn't, what surprised.\" }\n","parsed_content":{"meta":{"notes":"Per spec constraint: NO LinkedIn scraping. Founder research draws\nfrom the alternative source taxonomy below. Per spec policy: the\nagent compiles research material on founders but does NOT score\nthe team dimension — that remains partner-only (see rubric.yaml).\n","owner":"vendor","version":"0.1","last_updated":"2026-05-06"},"finding_record":{"fields":[{"name":"id","note":"Stable finding ID, e.g. 'finding_market_001'.","type":"string","required":true},{"name":"category","note":"ID from research_categories.","type":"string","required":true},{"name":"text","note":"The finding, phrased neutrally.","type":"string","required":true},{"name":"finding_type","note":"fact = independently established; signal = directional but not conclusive; opinion = third-party interpretation; datapoint = a specific number; contradiction = directly contradicts a company claim.","type":"enum","values":["fact","signal","opinion","datapoint","contradiction"],"required":true},{"name":"source_url","note":"Direct URL. If multiple sources, the primary.","type":"url","required":true},{"name":"source_publisher","note":"e.g., 'Bloomberg', 'SEC EDGAR', 'GitHub'.","type":"string","required":true},{"name":"source_date","note":"Publication date. Recency matters; flag if >18 months old.","type":"date","required":false},{"name":"source_quality_tier","type":"enum","values":["tier_1_authoritative","tier_2_credible","tier_3_signal","tier_4_weak"],"required":true},{"name":"corroborating_sources","note":"Additional URLs supporting the same finding. Multi-source raises confidence.","type":"list","required":false},{"name":"confidence","type":"enum","values":["low","medium","high"],"required":true},{"name":"confidence_rationale","note":"Why this confidence level — source tier, corroboration, recency, methodology.","type":"string","required":false},{"name":"feeds_memo_section","note":"Memo sections this finding is relevant to.","type":"list","required":true},{"name":"relates_to_claim_id","note":"If this finding bears on a specific data-room claim, reference it here.","type":"string","required":false},{"name":"relationship_to_claim","note":"How this finding relates to the company-stated claim.","type":"enum","values":["confirms","partially_confirms","contradicts","contextualizes","unrelated"],"required":false}],"description":"A discrete piece of external information the agent has gathered."},"research_output":{"fields":[{"name":"dossier_id","type":"string","required":true},{"name":"research_date","type":"datetime","required":true},{"name":"findings","note":"All finding_record entries.","type":"list","required":true},{"name":"contradictions","note":"All contradiction_record entries.","type":"list","required":true},{"name":"competitive_map","note":"Output of competitive_research_special_handling.","type":"object","required":true},{"name":"founder_dossiers","note":"One per founder; compiled material only.","type":"list","required":true},{"name":"claim_verifications","note":"Updates to data_room_ingestion claim verification_status fields.","type":"list","required":true},{"name":"research_gaps","note":"Areas where the agent could not find independent sourcing — surfaced for partner attention.","type":"list","required":false},{"name":"agent_notes","note":"Neutral overall observations: what was easy to verify, what wasn't, what surprised.","type":"string","required":false}]},"research_categories":[{"id":"market","name":"Market validation","description":"Independent verification of market size, growth, and dynamics claimed by the company.","feeds_dimensions":["market"]},{"id":"competitive","name":"Competitive landscape","description":"Direct, adjacent, and incumbent competitors. Includes competitors the company did not mention — usually the most valuable output of this category.","feeds_dimensions":["competition_moat","product_technology"]},{"id":"founder_team","name":"Founder and team research","description":"Background, prior work, public statements, technical output. Compiled for partner judgment; never scored by the agent.","feeds_dimensions":["team"]},{"id":"customer_validation","name":"Customer and demand validation","description":"Independent signals of customer demand or product reception — reviews, forums, public references.","feeds_dimensions":["traction","product_technology"]},{"id":"regulatory","name":"Regulatory and policy environment","description":"Tailwinds, headwinds, pending legislation, compliance requirements.","feeds_dimensions":["market","business_model"]},{"id":"technical_validation","name":"Technical validation","description":"Independent assessment of technical claims — papers, benchmarks, engineering output.","feeds_dimensions":["product_technology","competition_moat"]}],"contradiction_record":{"fields":[{"name":"id","type":"string","required":true},{"name":"company_claim_id","note":"Claim ID from data_room_ingestion.","type":"string","required":true},{"name":"company_claim_text","note":"Restated for partner readability.","type":"string","required":true},{"name":"contradicting_finding_ids","type":"list","required":true},{"name":"nature","note":"factual = numbers don't match; definitional = different definition of the same term; methodological = different measurement approach; temporal = the claim was true at one time but isn't now; scope = the claim is true for a subset but stated as universal.","type":"enum","values":["factual","definitional","methodological","temporal","scope"],"required":true},{"name":"severity","note":"How much it should affect partner judgment.","type":"enum","values":["minor","moderate","material"],"required":true},{"name":"agent_notes","note":"Neutral framing of the discrepancy. Do not editorialize on intent.","type":"string","required":false}],"rationale":"When external evidence contradicts a company-stated claim, that's\nthe highest-value output of the research stage. Partners need\nthese surfaced clearly, not buried in a flat list of findings.\nEvery contradiction also writes back to the originating claim's\nverification_status (set to 'contradicted' in the ingestion\noutput).\n"},"source_quality_tiers":{"tier_4_weak":{"examples":["Reddit, Hacker News, Twitter/X threads","Glassdoor reviews","Anonymous forum posts","Aggregator and SEO-optimized listicles"],"description":"Use only with corroboration. Never the sole source for a material claim.","default_confidence":"low"},"tier_3_signal":{"examples":["Company website and marketing materials (third-party companies — not the target company)","Podcast interviews, conference talks","Founder personal blogs, Substacks","Product Hunt, G2, Capterra reviews (signal, not statistic)","GitHub repositories and release history"],"description":"Useful as signal of sentiment or surface-level fact; not relied on alone for material claims.","default_confidence":"medium"},"tier_excluded":{"examples":["LinkedIn scraping (per spec)","Sites that require authentication or bypass paywalls","Dark social, leaked private data","Sites flagged for misinformation or extremism"],"description":"Never used."},"tier_2_credible":{"examples":["Established business journalism (FT, WSJ, Bloomberg, Reuters, The Information)","Vertical trade press with reputation","Crunchbase, PitchBook (for funding/round data)","Company press releases and SEC filings (for that company's own facts)"],"description":"Established journalism and research with editorial standards but secondary.","default_confidence":"medium_to_high"},"tier_1_authoritative":{"examples":["Government data (Census, BLS, SEC filings, regulatory filings)","Peer-reviewed research","Major analyst firms with disclosed methodology (Gartner, Forrester, IDC)","Industry associations with published methodology","Court records, patent filings"],"description":"Primary or near-primary sources with strong editorial or methodological standards.","default_confidence":"high"}},"founder_research_special_handling":{"in_scope":["Public professional history from company websites, press, official bios","Published work: papers, patents, talks, books, podcasts","Open source contributions (GitHub, package registries)","Public statements: interviews, panel appearances, public letters","Verifiable affiliations: prior companies, advisor roles, board seats","Press mentions including coverage of prior ventures, exits, lawsuits","Court records (subject to ethical and legal considerations)","Educational credentials where publicly verified"],"rationale":"Constraints: no LinkedIn scraping; team dimension is partner-only;\nthe agent compiles material but does not interpret character. This\nsection defines what's in-scope, what's out-of-scope, and the\nalternative source taxonomy that fills the gap LinkedIn would\notherwise occupy.\n","out_of_scope":["LinkedIn scraping (per spec constraint)","Personal social media used for personal life (non-professional content)","Speculation about personal relationships, family, or health","Inference from photos, lifestyle, or appearance","Anonymous accusations without corroboration","Any character interpretation — the agent surfaces material; the partner interprets"],"output_constraints":["Compile material. Do not interpret character.","Direct quotes only when source is reliable; paraphrase otherwise.","Flag inconsistencies between data room founder bios and external sources.","Flag prior ventures the company did not mention.","Never state or imply a personal characteristic that isn't directly evidenced."],"alternative_source_taxonomy":{"primary_sources":[{"id":"company_history","name":"Prior company websites and About pages","priority":"high"},{"id":"press_coverage","name":"Press coverage of prior roles and exits","priority":"high"},{"id":"government_filings","name":"SEC, Companies House, regulatory filings","note":"For director histories and prior ventures.","priority":"high"},{"id":"court_records","name":"Public court records","note":"Use cautiously; pending matters are not findings.","priority":"medium"},{"id":"patent_filings","name":"USPTO, EPO, WIPO","priority":"medium"},{"id":"academic_publications","name":"Google Scholar, arXiv, peer-reviewed work","note":"For technical founders.","priority":"high"},{"id":"open_source","name":"GitHub, package registries","note":"For technical founders — code is the strongest available signal.","priority":"high"},{"id":"conference_talks","name":"YouTube talks, conference recordings","priority":"medium"},{"id":"podcast_interviews","name":"Podcast appearances","note":"Often more candid than written press.","priority":"medium"},{"id":"long_form_interviews","name":"Magazine profiles, deep-dive articles","priority":"medium"},{"id":"founder_writing","name":"Personal blogs, Substacks, op-eds","note":"Distinguish between personal voice and ghostwritten content.","priority":"medium"}],"backchannel_signals":{"description":"Your CRM's connection mapping surfaces who in the partner network has worked with these founders. The agent identifies the connections; the partner does the actual outreach. Backchannel content is never reported in the dossier as a finding — it is collected by the partner and entered through Q&A.","agent_action":"Surface the connection list with prior affiliations. Do not contact, do not summarize what others might say."}}},"competitive_research_special_handling":{"rationale":"The most valuable output of competitive research is usually the\ncompetitors the company did NOT mention. Companies systematically\nunder-represent competition in pitch materials. This section\nforces the agent to look beyond the company-supplied list.\n","required_outputs":[{"id":"company_named_competitors","name":"Competitors named by the company","required":true},{"id":"company_unnamed_competitors","name":"Competitors NOT named by the company but operating in the same space","note":"Highest-value output of this stage.","required":true},{"id":"adjacent_competitors","name":"Adjacent players who could enter","required":true},{"id":"incumbent_competitors","name":"Incumbents who already serve this need (often with worse products)","required":true},{"id":"build_vs_buy_alternatives","name":"What customers do today instead of buying anything","note":"Sometimes 'a spreadsheet' is the real competitor.","required":false}],"per_competitor_record":{"fields":[{"name":"id","type":"string"},{"name":"name","type":"string"},{"name":"category","type":"enum","values":["direct","adjacent","incumbent","alternative"]},{"name":"company_mentioned","note":"Did the company name them in the data room?","type":"boolean"},{"name":"positioning_summary","note":"1–2 sentence neutral description of what they do.","type":"string"},{"name":"stage","type":"enum","values":["seed","series_a","series_b","growth","public","private_mature","unknown"]},{"name":"most_recent_funding","note":"Round, amount, date, lead — when known.","type":"object","required":false},{"name":"differentiation_vs_target","note":"How target company is positioned differently. Neutral framing.","type":"string"},{"name":"source_urls","type":"list"}]}}},"is_active":true,"edit_note":"Initial defaults from lib/memo-agent/defaults","edited_by":null,"edited_at":"2026-05-11T02:25:23.546365+00:00","created_at":"2026-05-11T02:25:23.546365+00:00"},"memo_output":{"id":"0429fb3a-a1d4-4c0f-ab01-680577474556","fund_id":"e2dfd2bf-ced3-4647-8277-096e616a6eab","schema_name":"memo_output","schema_version":"v0.1","yaml_content":"# Memo Agent — Memo Output Schema\n# Version: 0.1 (draft)\n# Used by: Memo Drafting Agent (Stage 4 — drafting)\n#\n# The agent produces a STRUCTURED object, not a flat document. A\n# separate rendering step converts the structure to a Word doc / Google\n# Doc with inline citations for partner review. Storing the structured\n# intermediate makes provenance, versioning, and partner edits tractable.\n#\n# Source IDs in this file reference:\n#   - claim_id        from data_room_ingestion.yaml\n#   - finding_id      from research_dossier.yaml\n#   - qa_answer_id    captured from partner Q&A (Stage 3)\n#   - assumption_id   from data_room_ingestion.financial_extraction\n#   - dimension_id    from rubric.yaml\n\nmeta:\n  version: \"0.2\"\n  last_updated: \"2026-05-10\"\n  owner: partners (section structure) + vendor (technical fields)\n  notes: |\n    The agent never produces a \"final\" memo — only drafts. The schema\n    enforces this. There is no field for \"final recommendation\" or\n    \"approved.\" Partners produce the recommendation; the renderer\n    marks every memo as DRAFT until partners explicitly finalize.\n\n# ---------------------------------------------------------------------------\n# Memo structure — sections, order, sources, partner-only enforcement\n# ---------------------------------------------------------------------------\n\nmemo_structure:\n  description: |\n    Section list. Order matters — partners read top to bottom. Each\n    section declares which inputs feed it and which fields are\n    partner-only.\n\n  sections:\n\n    - id: header\n      title: \"Header\"\n      kind: metadata\n      fields: [company_name, sector, stage, round_size, deal_lead, memo_date, draft_version, agent_version]\n      partner_only_fields: [deal_lead]\n\n    - id: executive_summary\n      title: \"Executive Summary\"\n      kind: prose\n      max_paragraphs: 3\n      sources: [claims, findings, scores, qa_answers]\n      guidance: |\n        Headlines the opportunity, the key risks, and the open questions.\n        Does NOT include a recommendation. Does NOT use superlatives or\n        marketing voice. If the company-stated claims and the external\n        findings disagree on a material fact, the executive summary\n        surfaces that disagreement.\n\n    - id: recommendation\n      title: \"Recommendation\"\n      kind: partner_only\n      agent_behavior: |\n        Leave empty. Insert placeholder: \"[Partner to complete]\". Do\n        not draft a recommendation, not even tentatively, not even as\n        a \"for discussion\" suggestion.\n\n    - id: company_overview\n      title: \"Company Overview\"\n      kind: prose\n      sources: [claims]\n      guidance: \"Neutral description from data room. What they do, who they sell to, where they are.\"\n\n    - id: product\n      title: \"Product\"\n      kind: prose\n      sources: [claims, findings]\n      feeds_score: product_technology\n      guidance: |\n        What the product actually does, who uses it, the core user\n        flow, and concrete differentiation versus what existed before.\n        Describe screenshots/demos from the data room when present.\n        If the product is in early development, say so explicitly and\n        cite what exists today versus what's promised on the roadmap.\n        Defensibility and technical moat live here too — what's hard\n        about what they're building and why hasn't it been done.\n\n    - id: market\n      title: \"Market\"\n      kind: prose\n      sources: [claims, findings]\n      feeds_score: market\n      guidance: |\n        State market size and growth with sourcing. If TAM/SAM/SOM is\n        company-stated only, say so explicitly. Independent third-party\n        sources elevate confidence; their absence lowers it.\n\n    - id: traction\n      title: \"Traction & Evidence\"\n      kind: prose\n      sources: [claims, findings]\n      feeds_score: traction\n      guidance: |\n        Distinguish company-stated metrics from independently verified\n        ones. Customer references compiled from research are surfaced\n        as findings, not as facts.\n\n    - id: business_model\n      title: \"Business Model & Financials\"\n      kind: prose\n      sources: [claims, findings, financial_assumptions]\n      feeds_score: business_model\n      guidance: |\n        Every projected number must be marked as a projection.\n        Aggressive assumptions (per data_room_ingestion.financial_extraction)\n        appear inline next to the metrics they drive. Do not editorialize\n        on whether assumptions are achievable; surface the comparison\n        to historical actuals.\n\n    - id: team\n      title: \"Team\"\n      kind: hybrid\n      partner_only_fields: [character_assessment, founder_market_fit_judgment]\n      machine_fields: [factual_summary, prior_work, public_output, references_to_qa]\n      sources: [qa_answers, founder_research]\n      feeds_score: team\n      guidance: |\n        Compile factual material from founder research. Surface partner\n        Q&A answers verbatim where the partner has provided them. Do\n        not score the team. Do not interpret character. Flag any\n        inconsistencies between data room founder bios and external\n        sources for partner attention.\n\n    - id: competition_moat\n      title: \"Competition & Moat\"\n      kind: prose\n      sources: [claims, findings]\n      feeds_score: competition_moat\n      guidance: |\n        Required to address competitors NOT named by the company. If\n        the agent could not find any, state that explicitly rather\n        than implying the named list is exhaustive.\n\n    - id: outcomes_analysis\n      title: \"Outcomes Analysis\"\n      kind: hybrid\n      partner_only_fields: [base_case_judgment, upside_thesis, downside_floor, target_ownership]\n      machine_fields: [comparable_exits, revenue_projections_at_exit, implied_valuations]\n      sources: [claims, findings, financial_assumptions]\n      guidance: |\n        Lay out the return scenarios the fund is underwriting to.\n        Pull projected revenue at exit (typically year 5–7) from\n        financial_assumptions and surface the company's own targets\n        alongside any historical trajectory. Compile comparable exits\n        from research findings — recent M&A or IPO outcomes in this\n        space at similar revenue scale, with their exit valuations\n        and revenue multiples. Compute implied exit valuations and\n        the resulting fund return at the current entry valuation for\n        base / upside / downside scenarios. Every forward number is\n        a projection (contains_projection = true). The partner-only\n        fields capture the actual underwriting judgment — agent does\n        not write what to underwrite to, only the inputs.\n\n    - id: deal_terms\n      title: \"Deal & Terms\"\n      kind: hybrid\n      partner_only_fields: [terms_judgment, comp_analysis_conclusion]\n      sources: [claims, findings]\n      feeds_score: deal_terms\n\n    - id: risks_and_open_questions\n      title: \"Risks & Open Questions\"\n      kind: prose\n      sources: [claims, findings, contradictions, gaps, partner_attention]\n      guidance: |\n        Pulls directly from contradictions, low-confidence scores,\n        gap_analysis output, and unverified material claims. Aggressive\n        assumptions surface here too.\n\n    - id: scoring_summary\n      title: \"Scoring Summary\"\n      kind: structured\n      content: score_block_per_dimension\n      sources: [rubric]\n\n    - id: appendix\n      title: \"Appendix — Sources & Citations\"\n      kind: structured\n      content: citation_map\n\n# ---------------------------------------------------------------------------\n# Paragraph record — the unit of provenance\n# ---------------------------------------------------------------------------\n\nparagraph_record:\n  description: |\n    Every paragraph in a prose section is a paragraph_record. Sources\n    are required. A paragraph with no sources is either partner_only\n    or it shouldn't exist.\n  fields:\n    - { name: id,                        type: string,  required: true }\n    - { name: section_id,                type: string,  required: true }\n    - { name: order,                     type: integer, required: true }\n    - { name: prose,                     type: string,  required: true }\n    - { name: sources,                   type: list,    required: true,  note: \"List of source_reference objects (below). Empty list ONLY allowed when origin = partner_only.\" }\n    - { name: origin,                    type: enum,    required: true,  values: [agent_drafted, partner_drafted, partner_only_placeholder, partner_edited] }\n    - { name: confidence,                type: enum,    required: true,  values: [low, medium, high, n/a] }\n    - { name: contains_projection,       type: boolean, required: true,  note: \"True if any forward-looking number appears. Renderer marks the paragraph accordingly.\" }\n    - { name: contains_unverified_claim, type: boolean, required: true,  note: \"True if any source has verification_status of 'unverified' or 'verification_attempted'.\" }\n    - { name: contains_contradiction,    type: boolean, required: true,  note: \"True if the paragraph references a contradiction record.\" }\n\nsource_reference:\n  description: \"How a paragraph cites a source.\"\n  fields:\n    - { name: source_type, type: enum,   values: [claim, finding, qa_answer, assumption, partner_only, gap] }\n    - { name: source_id,   type: string, note: \"ID from the corresponding schema. Required unless source_type = partner_only.\" }\n    - { name: span,        type: string, required: false, note: \"Optional inline span the source supports — for sentence-level attribution within a paragraph.\" }\n\n# ---------------------------------------------------------------------------\n# Score block — per dimension, pulled from rubric.yaml\n# ---------------------------------------------------------------------------\n\nscore_block:\n  description: \"Per-dimension scoring output. Renders into the Scoring Summary section.\"\n  fields:\n    - { name: dimension_id,           type: string,   required: true,  note: \"ID from rubric.yaml.\" }\n    - { name: mode,                   type: enum,     required: true,  values: [machine, hybrid, partner_only], note: \"Echoed from rubric — agent does not change this.\" }\n    - { name: score,                  type: integer,  required: false, note: \"1–5. NULL when mode = partner_only or when confidence is too low to score.\" }\n    - { name: confidence,             type: enum,     required: false, values: [low, medium, high], note: \"NULL when mode = partner_only.\" }\n    - { name: rationale,              type: string,   required: true,  note: \"1–3 sentence explanation. For partner_only, this is the agent's compiled supporting material, not a score justification.\" }\n    - { name: supporting_paragraphs,  type: list,     required: true,  note: \"Paragraph IDs that feed this score.\" }\n    - { name: low_confidence_flags,   type: list,     required: false, note: \"Which conditions from rubric.flag_low_confidence_when triggered.\" }\n    - { name: partner_override,       type: object,   required: false, note: \"Populated only when partner adjusts. Captures original score, override score, override rationale, override timestamp.\" }\n\n# ---------------------------------------------------------------------------\n# Partner attention — items requiring partner action before finalization\n# ---------------------------------------------------------------------------\n\npartner_attention:\n  description: |\n    Surfaces issues the partner must address before the memo can be\n    finalized. Drawn from contradictions, low-confidence scores,\n    gaps, missing Q&A, and aggressive assumptions. This is the\n    \"what's still unresolved\" list.\n  item_record:\n    fields:\n      - { name: id,        type: string,  required: true }\n      - { name: kind,      type: enum,    required: true,  values: [unverified_material_claim, contradiction, data_room_gap, low_confidence_score, missing_qa, aggressive_assumption, partner_only_blank] }\n      - { name: urgency,   type: enum,    required: true,  values: [must_address, should_address, fyi] }\n      - { name: text,      type: string,  required: true }\n      - { name: links,     type: list,    required: true,  note: \"Source IDs linking back to the originating record.\" }\n      - { name: status,    type: enum,    required: true,  values: [open, addressed, deferred], default: open }\n\n# ---------------------------------------------------------------------------\n# Citation map — appendix\n# ---------------------------------------------------------------------------\n\ncitation_map:\n  description: |\n    Every source ID referenced anywhere in the memo, with full\n    context. The renderer uses this to produce footnotes that link\n    each in-memo citation to its origin. Partners can audit any\n    sentence back to its source in one click.\n  entry_record:\n    fields:\n      - { name: source_id,         type: string,  required: true }\n      - { name: source_type,       type: enum,    required: true,  values: [claim, finding, qa_answer, assumption, gap] }\n      - { name: text,              type: string,  required: true,  note: \"The assertion as captured in its origin schema.\" }\n      - { name: origin_document,   type: string,  required: false, note: \"For claims: data room file name + location. For findings: source URL + publisher.\" }\n      - { name: confidence,        type: enum,    required: false, values: [low, medium, high] }\n      - { name: verification_status, type: enum,  required: false, values: [unverified, verification_attempted, verified, contradicted, unverifiable], note: \"Echoed from claim record.\" }\n\n# ---------------------------------------------------------------------------\n# Final assembly — what the agent hands off\n# ---------------------------------------------------------------------------\n\nmemo_draft_output:\n  description: |\n    Complete agent output for one memo draft. The agent produces this\n    object; the renderer converts to Word/Doc. Every field below is\n    structured; nothing is freeform document text outside paragraph\n    records.\n  fields:\n    - { name: draft_id,         type: string,    required: true }\n    - { name: deal_id,          type: string,    required: true }\n    - { name: draft_date,       type: datetime,  required: true }\n    - { name: draft_version,    type: string,    required: true,  note: \"e.g., 'v0.1', 'v0.2 — incorporated partner Q&A'.\" }\n    - { name: agent_version,    type: string,    required: true,  note: \"Schema and prompt version used to produce this draft.\" }\n    - { name: header,           type: object,    required: true }\n    - { name: sections,         type: list,      required: true,  note: \"Ordered list of section objects.\" }\n    - { name: paragraphs,       type: list,      required: true,  note: \"All paragraph_record entries, flat.\" }\n    - { name: scores,           type: list,      required: true,  note: \"All score_block entries — one per rubric dimension.\" }\n    - { name: partner_attention,type: list,      required: true }\n    - { name: citation_map,     type: list,      required: true }\n    - { name: is_draft,         type: boolean,   required: true,  default: true,  note: \"Always true from the agent. Cannot be set to false by the agent under any circumstance.\" }\n    - { name: agent_notes,      type: string,    required: false, note: \"Agent's overall observations: what was hard, what was missing, what the agent is least confident about.\" }\n\n# ---------------------------------------------------------------------------\n# Hard rules — structural enforcement of the 'draft, don't decide' principle\n# ---------------------------------------------------------------------------\n\nhard_rules:\n  - id: r_001\n    rule: \"is_draft is always true in agent output. The agent has no path to set it false.\"\n  - id: r_002\n    rule: \"The recommendation section is partner_only. Agent leaves placeholder; never drafts even as a suggestion.\"\n  - id: r_003\n    rule: \"Every paragraph_record has at least one source unless origin = partner_only_placeholder.\"\n  - id: r_004\n    rule: \"Team dimension score is always NULL in agent output (mode = partner_only per rubric.yaml).\"\n  - id: r_005\n    rule: \"Every projection appears as a paragraph with contains_projection = true. Renderer marks it visibly.\"\n  - id: r_006\n    rule: \"Contradictions surface in BOTH the relevant section AND the partner_attention list. Never only one.\"\n  - id: r_007\n    rule: \"If the agent cannot find independent verification of a material claim, the paragraph carrying that claim has confidence = low and contains_unverified_claim = true.\"\n  - id: r_008\n    rule: \"Citation map entries are required for every source_id referenced in any paragraph. No floating citations.\"\n","parsed_content":{"meta":{"notes":"The agent never produces a \"final\" memo — only drafts. The schema\nenforces this. There is no field for \"final recommendation\" or\n\"approved.\" Partners produce the recommendation; the renderer\nmarks every memo as DRAFT until partners explicitly finalize.\n","owner":"partners (section structure) + vendor (technical fields)","version":"0.2","last_updated":"2026-05-10"},"hard_rules":[{"id":"r_001","rule":"is_draft is always true in agent output. The agent has no path to set it false."},{"id":"r_002","rule":"The recommendation section is partner_only. Agent leaves placeholder; never drafts even as a suggestion."},{"id":"r_003","rule":"Every paragraph_record has at least one source unless origin = partner_only_placeholder."},{"id":"r_004","rule":"Team dimension score is always NULL in agent output (mode = partner_only per rubric.yaml)."},{"id":"r_005","rule":"Every projection appears as a paragraph with contains_projection = true. Renderer marks it visibly."},{"id":"r_006","rule":"Contradictions surface in BOTH the relevant section AND the partner_attention list. Never only one."},{"id":"r_007","rule":"If the agent cannot find independent verification of a material claim, the paragraph carrying that claim has confidence = low and contains_unverified_claim = true."},{"id":"r_008","rule":"Citation map entries are required for every source_id referenced in any paragraph. No floating citations."}],"score_block":{"fields":[{"name":"dimension_id","note":"ID from rubric.yaml.","type":"string","required":true},{"name":"mode","note":"Echoed from rubric — agent does not change this.","type":"enum","values":["machine","hybrid","partner_only"],"required":true},{"name":"score","note":"1–5. NULL when mode = partner_only or when confidence is too low to score.","type":"integer","required":false},{"name":"confidence","note":"NULL when mode = partner_only.","type":"enum","values":["low","medium","high"],"required":false},{"name":"rationale","note":"1–3 sentence explanation. For partner_only, this is the agent's compiled supporting material, not a score justification.","type":"string","required":true},{"name":"supporting_paragraphs","note":"Paragraph IDs that feed this score.","type":"list","required":true},{"name":"low_confidence_flags","note":"Which conditions from rubric.flag_low_confidence_when triggered.","type":"list","required":false},{"name":"partner_override","note":"Populated only when partner adjusts. Captures original score, override score, override rationale, override timestamp.","type":"object","required":false}],"description":"Per-dimension scoring output. Renders into the Scoring Summary section."},"citation_map":{"description":"Every source ID referenced anywhere in the memo, with full\ncontext. The renderer uses this to produce footnotes that link\neach in-memo citation to its origin. Partners can audit any\nsentence back to its source in one click.\n","entry_record":{"fields":[{"name":"source_id","type":"string","required":true},{"name":"source_type","type":"enum","values":["claim","finding","qa_answer","assumption","gap"],"required":true},{"name":"text","note":"The assertion as captured in its origin schema.","type":"string","required":true},{"name":"origin_document","note":"For claims: data room file name + location. For findings: source URL + publisher.","type":"string","required":false},{"name":"confidence","type":"enum","values":["low","medium","high"],"required":false},{"name":"verification_status","note":"Echoed from claim record.","type":"enum","values":["unverified","verification_attempted","verified","contradicted","unverifiable"],"required":false}]}},"memo_structure":{"sections":[{"id":"header","kind":"metadata","title":"Header","fields":["company_name","sector","stage","round_size","deal_lead","memo_date","draft_version","agent_version"],"partner_only_fields":["deal_lead"]},{"id":"executive_summary","kind":"prose","title":"Executive Summary","sources":["claims","findings","scores","qa_answers"],"guidance":"Headlines the opportunity, the key risks, and the open questions.\nDoes NOT include a recommendation. Does NOT use superlatives or\nmarketing voice. If the company-stated claims and the external\nfindings disagree on a material fact, the executive summary\nsurfaces that disagreement.\n","max_paragraphs":3},{"id":"recommendation","kind":"partner_only","title":"Recommendation","agent_behavior":"Leave empty. Insert placeholder: \"[Partner to complete]\". Do\nnot draft a recommendation, not even tentatively, not even as\na \"for discussion\" suggestion.\n"},{"id":"company_overview","kind":"prose","title":"Company Overview","sources":["claims"],"guidance":"Neutral description from data room. What they do, who they sell to, where they are."},{"id":"product","kind":"prose","title":"Product","sources":["claims","findings"],"guidance":"What the product actually does, who uses it, the core user\nflow, and concrete differentiation versus what existed before.\nDescribe screenshots/demos from the data room when present.\nIf the product is in early development, say so explicitly and\ncite what exists today versus what's promised on the roadmap.\nDefensibility and technical moat live here too — what's hard\nabout what they're building and why hasn't it been done.\n","feeds_score":"product_technology"},{"id":"market","kind":"prose","title":"Market","sources":["claims","findings"],"guidance":"State market size and growth with sourcing. If TAM/SAM/SOM is\ncompany-stated only, say so explicitly. Independent third-party\nsources elevate confidence; their absence lowers it.\n","feeds_score":"market"},{"id":"traction","kind":"prose","title":"Traction & Evidence","sources":["claims","findings"],"guidance":"Distinguish company-stated metrics from independently verified\nones. Customer references compiled from research are surfaced\nas findings, not as facts.\n","feeds_score":"traction"},{"id":"business_model","kind":"prose","title":"Business Model & Financials","sources":["claims","findings","financial_assumptions"],"guidance":"Every projected number must be marked as a projection.\nAggressive assumptions (per data_room_ingestion.financial_extraction)\nappear inline next to the metrics they drive. Do not editorialize\non whether assumptions are achievable; surface the comparison\nto historical actuals.\n","feeds_score":"business_model"},{"id":"team","kind":"hybrid","title":"Team","sources":["qa_answers","founder_research"],"guidance":"Compile factual material from founder research. Surface partner\nQ&A answers verbatim where the partner has provided them. Do\nnot score the team. Do not interpret character. Flag any\ninconsistencies between data room founder bios and external\nsources for partner attention.\n","feeds_score":"team","machine_fields":["factual_summary","prior_work","public_output","references_to_qa"],"partner_only_fields":["character_assessment","founder_market_fit_judgment"]},{"id":"competition_moat","kind":"prose","title":"Competition & Moat","sources":["claims","findings"],"guidance":"Required to address competitors NOT named by the company. If\nthe agent could not find any, state that explicitly rather\nthan implying the named list is exhaustive.\n","feeds_score":"competition_moat"},{"id":"outcomes_analysis","kind":"hybrid","title":"Outcomes Analysis","sources":["claims","findings","financial_assumptions"],"guidance":"Lay out the return scenarios the fund is underwriting to.\nPull projected revenue at exit (typically year 5–7) from\nfinancial_assumptions and surface the company's own targets\nalongside any historical trajectory. Compile comparable exits\nfrom research findings — recent M&A or IPO outcomes in this\nspace at similar revenue scale, with their exit valuations\nand revenue multiples. Compute implied exit valuations and\nthe resulting fund return at the current entry valuation for\nbase / upside / downside scenarios. Every forward number is\na projection (contains_projection = true). The partner-only\nfields capture the actual underwriting judgment — agent does\nnot write what to underwrite to, only the inputs.\n","machine_fields":["comparable_exits","revenue_projections_at_exit","implied_valuations"],"partner_only_fields":["base_case_judgment","upside_thesis","downside_floor","target_ownership"]},{"id":"deal_terms","kind":"hybrid","title":"Deal & Terms","sources":["claims","findings"],"feeds_score":"deal_terms","partner_only_fields":["terms_judgment","comp_analysis_conclusion"]},{"id":"risks_and_open_questions","kind":"prose","title":"Risks & Open Questions","sources":["claims","findings","contradictions","gaps","partner_attention"],"guidance":"Pulls directly from contradictions, low-confidence scores,\ngap_analysis output, and unverified material claims. Aggressive\nassumptions surface here too.\n"},{"id":"scoring_summary","kind":"structured","title":"Scoring Summary","content":"score_block_per_dimension","sources":["rubric"]},{"id":"appendix","kind":"structured","title":"Appendix — Sources & Citations","content":"citation_map"}],"description":"Section list. Order matters — partners read top to bottom. Each\nsection declares which inputs feed it and which fields are\npartner-only.\n"},"paragraph_record":{"fields":[{"name":"id","type":"string","required":true},{"name":"section_id","type":"string","required":true},{"name":"order","type":"integer","required":true},{"name":"prose","type":"string","required":true},{"name":"sources","note":"List of source_reference objects (below). Empty list ONLY allowed when origin = partner_only.","type":"list","required":true},{"name":"origin","type":"enum","values":["agent_drafted","partner_drafted","partner_only_placeholder","partner_edited"],"required":true},{"name":"confidence","type":"enum","values":["low","medium","high","n/a"],"required":true},{"name":"contains_projection","note":"True if any forward-looking number appears. Renderer marks the paragraph accordingly.","type":"boolean","required":true},{"name":"contains_unverified_claim","note":"True if any source has verification_status of 'unverified' or 'verification_attempted'.","type":"boolean","required":true},{"name":"contains_contradiction","note":"True if the paragraph references a contradiction record.","type":"boolean","required":true}],"description":"Every paragraph in a prose section is a paragraph_record. Sources\nare required. A paragraph with no sources is either partner_only\nor it shouldn't exist.\n"},"source_reference":{"fields":[{"name":"source_type","type":"enum","values":["claim","finding","qa_answer","assumption","partner_only","gap"]},{"name":"source_id","note":"ID from the corresponding schema. Required unless source_type = partner_only.","type":"string"},{"name":"span","note":"Optional inline span the source supports — for sentence-level attribution within a paragraph.","type":"string","required":false}],"description":"How a paragraph cites a source."},"memo_draft_output":{"fields":[{"name":"draft_id","type":"string","required":true},{"name":"deal_id","type":"string","required":true},{"name":"draft_date","type":"datetime","required":true},{"name":"draft_version","note":"e.g., 'v0.1', 'v0.2 — incorporated partner Q&A'.","type":"string","required":true},{"name":"agent_version","note":"Schema and prompt version used to produce this draft.","type":"string","required":true},{"name":"header","type":"object","required":true},{"name":"sections","note":"Ordered list of section objects.","type":"list","required":true},{"name":"paragraphs","note":"All paragraph_record entries, flat.","type":"list","required":true},{"name":"scores","note":"All score_block entries — one per rubric dimension.","type":"list","required":true},{"name":"partner_attention","type":"list","required":true},{"name":"citation_map","type":"list","required":true},{"name":"is_draft","note":"Always true from the agent. Cannot be set to false by the agent under any circumstance.","type":"boolean","default":true,"required":true},{"name":"agent_notes","note":"Agent's overall observations: what was hard, what was missing, what the agent is least confident about.","type":"string","required":false}],"description":"Complete agent output for one memo draft. The agent produces this\nobject; the renderer converts to Word/Doc. Every field below is\nstructured; nothing is freeform document text outside paragraph\nrecords.\n"},"partner_attention":{"description":"Surfaces issues the partner must address before the memo can be\nfinalized. Drawn from contradictions, low-confidence scores,\ngaps, missing Q&A, and aggressive assumptions. This is the\n\"what's still unresolved\" list.\n","item_record":{"fields":[{"name":"id","type":"string","required":true},{"name":"kind","type":"enum","values":["unverified_material_claim","contradiction","data_room_gap","low_confidence_score","missing_qa","aggressive_assumption","partner_only_blank"],"required":true},{"name":"urgency","type":"enum","values":["must_address","should_address","fyi"],"required":true},{"name":"text","type":"string","required":true},{"name":"links","note":"Source IDs linking back to the originating record.","type":"list","required":true},{"name":"status","type":"enum","values":["open","addressed","deferred"],"default":"open","required":true}]}}},"is_active":true,"edit_note":"Initial defaults from lib/memo-agent/defaults","edited_by":null,"edited_at":"2026-05-11T02:25:23.906165+00:00","created_at":"2026-05-11T02:25:23.906165+00:00"},"style_anchors":{"id":"583aef89-d566-48b7-918a-df1a1846f8b9","fund_id":"e2dfd2bf-ced3-4647-8277-096e616a6eab","schema_name":"style_anchors","schema_version":"v0.1","yaml_content":"# Memo Agent — Style Anchors Schema\n# Version: 0.1 (draft)\n# Used by: Memo Drafting Agent (Stage 4 — drafting; consulted at every memo run)\n#\n# Defines the metadata wrapper around uploaded reference memos. The\n# memos themselves are stored as PDF / DOCX / Markdown files —\n# this schema describes how each one is annotated, how the agent\n# uses them, and how to aggregate across multiple uploads.\n#\n# In a Claude Project: reference memos are uploaded as project\n# knowledge files; this schema describes the metadata that should\n# accompany each one (in a sibling .yaml or front-matter).\n#\n# In a deployed component: reference memos are rows in a\n# style_anchor_memos table; this schema is the canonical contract\n# for that table's columns.\n\nmeta:\n  version: \"0.1\"\n  last_updated: \"2026-05-06\"\n  owner: firm partners + vendor\n  notes: |\n    Reference memos are the highest-leverage configuration change\n    a firm can make to improve memo quality. They override the voice\n    defaults in instructions.md Section 6. See instructions.md\n    Section 7 for the agent's behavioral rules around using these.\n\n# ---------------------------------------------------------------------------\n# Usage principles — these are also stated in instructions.md but the\n# schema repeats the load-bearing ones so they're enforced at the data layer.\n# ---------------------------------------------------------------------------\n\nusage_principles:\n  - id: u_001\n    rule: \"Reference memos teach voice, structure, and analytical patterns. They never supply facts to a new memo.\"\n  - id: u_002\n    rule: \"The agent reads reference memos at the start of every memo run. This is in-context style learning, not fine-tuning.\"\n  - id: u_003\n    rule: \"When `memo_output.yaml` and the reference memos disagree on structure, the schema wins. Surface the discrepancy for partner review.\"\n  - id: u_004\n    rule: \"When reference memos disagree with each other, the agent matches the dominant cluster, not the average. Material conflicts surface to the partner rather than getting split.\"\n  - id: u_005\n    rule: \"Outdated surface vocabulary in reference memos is updated. Structural patterns are preserved.\"\n  - id: u_006\n    rule: \"All hard rules in instructions.md Section 3 still apply. A reference memo that breaks the rules (e.g., scores the team, drafts a recommendation) does not authorize the agent to do the same.\"\n\n# ---------------------------------------------------------------------------\n# Per-memo record — metadata wrapper around each uploaded memo\n# ---------------------------------------------------------------------------\n\nmemo_record:\n  description: |\n    Every uploaded reference memo carries this metadata. Some fields\n    are required (the agent needs them to weight correctly); most are\n    optional and improve quality when provided.\n  fields:\n    - { name: id,                    type: string,   required: true,  note: \"Stable ID. Suggested: 'anchor_<year>_<short-name>', e.g. 'anchor_2024_aurora_health'.\" }\n    - { name: file_reference,        type: string,   required: true,  note: \"Project knowledge filename (Claude Project) OR Supabase storage path (deployed). Points to the actual PDF/DOCX/MD.\" }\n    - { name: title,                 type: string,   required: false, note: \"Display title. Often the company name; can be anonymized.\" }\n    - { name: anonymized,            type: boolean,  required: false, default: false, note: \"True if names/figures have been redacted. The agent should not attempt to identify the company from context.\" }\n    - { name: vintage_year,          type: integer,  required: false, note: \"Year the memo was written. Used for recency weighting and to flag stale framings.\" }\n    - { name: vintage_quarter,       type: enum,     required: false, values: [Q1, Q2, Q3, Q4] }\n    - { name: sector,                type: string,   required: false, note: \"Free text. e.g., 'B2B SaaS — vertical CRM', 'consumer fintech', 'healthcare AI'.\" }\n    - { name: deal_stage_at_writing, type: enum,     required: false, values: [pre_seed, seed, series_a, series_b, growth, follow_on, other] }\n    - { name: outcome,               type: enum,     required: false, values: [invested, passed, lost_competitive, withdrew, unknown], note: \"What the firm did with this deal.\" }\n    - { name: conviction_at_writing, type: enum,     required: false, values: [high, medium, low, mixed], note: \"How strong was the firm's conviction at the time the memo was written. Useful: high-conviction memos often surface the firm's clearest analytical voice.\" }\n    - { name: voice_representativeness, type: enum,  required: false, default: representative, values: [exemplary, representative, atypical, do_not_match_voice], note: \"How well this memo represents the firm's typical voice. 'do_not_match_voice' = read for structure but ignore for voice.\" }\n    - { name: authorship,            type: enum,     required: false, values: [single_partner, lead_with_input, partnership, unknown] }\n    - { name: author_initials,       type: string,   required: false, note: \"Optional. Helps the agent group memos by author when matching voice.\" }\n    - { name: focus_attention_on,    type: list,     required: false, note: \"List of attention_taxonomy IDs — what to specifically attend to in this memo. Empty list = attend to everything equally.\" }\n    - { name: deprioritize_in_this_memo, type: list, required: false, note: \"List of attention_taxonomy IDs — what to NOT learn from this memo (e.g., 'market_sizing' if the memo's market analysis is dated).\" }\n    - { name: partner_notes,         type: string,   required: false, note: \"Free text guidance from the firm. e.g., 'This is the gold standard for how we treat team sections.' or 'Structure is right; voice is too formal — match content, not tone.'\" }\n\n# ---------------------------------------------------------------------------\n# Attention taxonomy — what aspects of a memo can be attended to or\n# deprioritized. Used in `focus_attention_on` and `deprioritize_in_this_memo`.\n# ---------------------------------------------------------------------------\n\nattention_taxonomy:\n\n  voice_dimensions:\n    - { id: register,                name: \"Formal vs. conversational register\" }\n    - { id: hedging,                 name: \"How the firm phrases uncertainty\" }\n    - { id: sentence_rhythm,         name: \"Sentence length, paragraph rhythm\" }\n    - { id: vocabulary,              name: \"Domain language and recurring framings\" }\n    - { id: declarative_vs_argued,   name: \"Stating conclusions vs. building to them\" }\n\n  structure_dimensions:\n    - { id: section_order,           name: \"Order of memo sections\" }\n    - { id: section_proportions,     name: \"Relative length of sections\" }\n    - { id: exec_summary_treatment,  name: \"What goes in the executive summary\" }\n    - { id: open_questions_treatment, name: \"How open questions are presented\" }\n    - { id: idiosyncratic_sections,  name: \"Firm-specific sections like 'Why now', 'What we'd need to believe'\" }\n\n  analytical_dimensions:\n    - { id: market_sizing,           name: \"Approach to market analysis\" }\n    - { id: team_section,            name: \"How the team is treated (within partner_only constraints)\" }\n    - { id: product_section,         name: \"Depth and framing of product/technology analysis\" }\n    - { id: traction_treatment,      name: \"How metrics and evidence are presented\" }\n    - { id: financial_treatment,     name: \"How projections and unit economics are discussed\" }\n    - { id: competitive_framing,     name: \"How competitors and moats are framed\" }\n    - { id: risks_treatment,         name: \"How risks are surfaced and weighed\" }\n    - { id: deal_terms_treatment,    name: \"How terms and structure are discussed\" }\n\n# ---------------------------------------------------------------------------\n# Aggregation rules — how the agent reconciles multiple reference memos\n# ---------------------------------------------------------------------------\n\naggregation:\n  default_weighting: equal\n  alternative_weightings:\n    - id: recency_weighted\n      description: \"Recent memos weighted more heavily. Useful when the firm's voice has evolved.\"\n    - id: conviction_weighted\n      description: \"High-conviction memos weighted more heavily. Useful when the firm has a clearest analytical voice on its strongest deals.\"\n    - id: partner_marked\n      description: \"Only memos with voice_representativeness in [exemplary, representative] inform voice. Others read for structure only.\"\n\n  conflict_resolution:\n    rule: |\n      When reference memos disagree on a structural or stylistic choice\n      (e.g., some have a \"Why now\" section and some don't), the agent:\n        1. Identifies the dominant cluster (most common pattern).\n        2. If the cluster is clear (>60% of memos), follows the cluster.\n        3. If no clear cluster, flags the divergence in agent_notes\n           and follows the structure in `memo_output.yaml`.\n        4. Never silently averages or invents a hybrid.\n\n  minimum_useful_count:\n    description: \"How many uploaded memos before voice-matching becomes reliable.\"\n    guidance: |\n      With 1 memo: voice is approximated; treat output as draft only.\n      With 3-5 memos: voice-matching becomes reliable for the dominant author.\n      With 8+ memos: aggregate patterns become robust across authors and vintages.\n    note: \"Below 3 memos, the agent's agent_notes should mention that the voice match is preliminary.\"\n\n# ---------------------------------------------------------------------------\n# What the agent extracts and writes to its working memory\n# ---------------------------------------------------------------------------\n\nstyle_synthesis:\n  description: |\n    At the start of every memo run, the agent reads all uploaded\n    reference memos (subject to weighting) and produces an internal\n    style synthesis it then applies during drafting (Stage 4) and\n    rendering (Stage 6). The synthesis is not user-facing but its\n    structure is documented here for auditability.\n  output_record:\n    fields:\n      - { name: synthesis_id,        type: string }\n      - { name: memos_consulted,     type: list,    note: \"Memo IDs used in this synthesis.\" }\n      - { name: weighting_used,      type: enum,    values: [equal, recency_weighted, conviction_weighted, partner_marked] }\n      - { name: voice_summary,       type: string,  note: \"1-2 paragraph internal description of the firm's voice.\" }\n      - { name: structure_summary,   type: string,  note: \"How sections are typically ordered, weighted, and titled.\" }\n      - { name: vocabulary_signals,  type: list,    note: \"Recurring phrases, framings, and rhetorical moves.\" }\n      - { name: identified_conflicts,type: list,    note: \"Cases where memos disagreed; how the agent resolved them.\" }\n      - { name: confidence,          type: enum,    values: [low, medium, high], note: \"How confident the agent is in the synthesis given the volume and consistency of inputs.\" }\n\n# ---------------------------------------------------------------------------\n# Guardrails — things the agent never does with reference memos\n# ---------------------------------------------------------------------------\n\nguardrails:\n  - id: g_001\n    rule: \"Never carry a fact, claim, statistic, or finding from a reference memo into a new draft. Reference memos describe past deals, not the current one.\"\n  - id: g_002\n    rule: \"Never reproduce a paragraph from a reference memo verbatim. Voice matching is structural and stylistic, not lexical copying.\"\n  - id: g_003\n    rule: \"Never use a reference memo to justify breaking a hard rule from instructions.md Section 3 — even if the reference memo itself broke that rule.\"\n  - id: g_004\n    rule: \"Never present the reference memos to the partner as if they were research findings on the current deal. Reference memos are voice training input only.\"\n  - id: g_005\n    rule: \"When uncertain whether a stylistic choice should be matched, prefer the defaults in instructions.md Section 6. The agent errs toward neutral.\"\n","parsed_content":{"meta":{"notes":"Reference memos are the highest-leverage configuration change\na firm can make to improve memo quality. They override the voice\ndefaults in instructions.md Section 6. See instructions.md\nSection 7 for the agent's behavioral rules around using these.\n","owner":"firm partners + vendor","version":"0.1","last_updated":"2026-05-06"},"guardrails":[{"id":"g_001","rule":"Never carry a fact, claim, statistic, or finding from a reference memo into a new draft. Reference memos describe past deals, not the current one."},{"id":"g_002","rule":"Never reproduce a paragraph from a reference memo verbatim. Voice matching is structural and stylistic, not lexical copying."},{"id":"g_003","rule":"Never use a reference memo to justify breaking a hard rule from instructions.md Section 3 — even if the reference memo itself broke that rule."},{"id":"g_004","rule":"Never present the reference memos to the partner as if they were research findings on the current deal. Reference memos are voice training input only."},{"id":"g_005","rule":"When uncertain whether a stylistic choice should be matched, prefer the defaults in instructions.md Section 6. The agent errs toward neutral."}],"aggregation":{"default_weighting":"equal","conflict_resolution":{"rule":"When reference memos disagree on a structural or stylistic choice\n(e.g., some have a \"Why now\" section and some don't), the agent:\n  1. Identifies the dominant cluster (most common pattern).\n  2. If the cluster is clear (>60% of memos), follows the cluster.\n  3. If no clear cluster, flags the divergence in agent_notes\n     and follows the structure in `memo_output.yaml`.\n  4. Never silently averages or invents a hybrid.\n"},"minimum_useful_count":{"note":"Below 3 memos, the agent's agent_notes should mention that the voice match is preliminary.","guidance":"With 1 memo: voice is approximated; treat output as draft only.\nWith 3-5 memos: voice-matching becomes reliable for the dominant author.\nWith 8+ memos: aggregate patterns become robust across authors and vintages.\n","description":"How many uploaded memos before voice-matching becomes reliable."},"alternative_weightings":[{"id":"recency_weighted","description":"Recent memos weighted more heavily. Useful when the firm's voice has evolved."},{"id":"conviction_weighted","description":"High-conviction memos weighted more heavily. Useful when the firm has a clearest analytical voice on its strongest deals."},{"id":"partner_marked","description":"Only memos with voice_representativeness in [exemplary, representative] inform voice. Others read for structure only."}]},"memo_record":{"fields":[{"name":"id","note":"Stable ID. Suggested: 'anchor_<year>_<short-name>', e.g. 'anchor_2024_aurora_health'.","type":"string","required":true},{"name":"file_reference","note":"Project knowledge filename (Claude Project) OR Supabase storage path (deployed). Points to the actual PDF/DOCX/MD.","type":"string","required":true},{"name":"title","note":"Display title. Often the company name; can be anonymized.","type":"string","required":false},{"name":"anonymized","note":"True if names/figures have been redacted. The agent should not attempt to identify the company from context.","type":"boolean","default":false,"required":false},{"name":"vintage_year","note":"Year the memo was written. Used for recency weighting and to flag stale framings.","type":"integer","required":false},{"name":"vintage_quarter","type":"enum","values":["Q1","Q2","Q3","Q4"],"required":false},{"name":"sector","note":"Free text. e.g., 'B2B SaaS — vertical CRM', 'consumer fintech', 'healthcare AI'.","type":"string","required":false},{"name":"deal_stage_at_writing","type":"enum","values":["pre_seed","seed","series_a","series_b","growth","follow_on","other"],"required":false},{"name":"outcome","note":"What the firm did with this deal.","type":"enum","values":["invested","passed","lost_competitive","withdrew","unknown"],"required":false},{"name":"conviction_at_writing","note":"How strong was the firm's conviction at the time the memo was written. Useful: high-conviction memos often surface the firm's clearest analytical voice.","type":"enum","values":["high","medium","low","mixed"],"required":false},{"name":"voice_representativeness","note":"How well this memo represents the firm's typical voice. 'do_not_match_voice' = read for structure but ignore for voice.","type":"enum","values":["exemplary","representative","atypical","do_not_match_voice"],"default":"representative","required":false},{"name":"authorship","type":"enum","values":["single_partner","lead_with_input","partnership","unknown"],"required":false},{"name":"author_initials","note":"Optional. Helps the agent group memos by author when matching voice.","type":"string","required":false},{"name":"focus_attention_on","note":"List of attention_taxonomy IDs — what to specifically attend to in this memo. Empty list = attend to everything equally.","type":"list","required":false},{"name":"deprioritize_in_this_memo","note":"List of attention_taxonomy IDs — what to NOT learn from this memo (e.g., 'market_sizing' if the memo's market analysis is dated).","type":"list","required":false},{"name":"partner_notes","note":"Free text guidance from the firm. e.g., 'This is the gold standard for how we treat team sections.' or 'Structure is right; voice is too formal — match content, not tone.'","type":"string","required":false}],"description":"Every uploaded reference memo carries this metadata. Some fields\nare required (the agent needs them to weight correctly); most are\noptional and improve quality when provided.\n"},"style_synthesis":{"description":"At the start of every memo run, the agent reads all uploaded\nreference memos (subject to weighting) and produces an internal\nstyle synthesis it then applies during drafting (Stage 4) and\nrendering (Stage 6). The synthesis is not user-facing but its\nstructure is documented here for auditability.\n","output_record":{"fields":[{"name":"synthesis_id","type":"string"},{"name":"memos_consulted","note":"Memo IDs used in this synthesis.","type":"list"},{"name":"weighting_used","type":"enum","values":["equal","recency_weighted","conviction_weighted","partner_marked"]},{"name":"voice_summary","note":"1-2 paragraph internal description of the firm's voice.","type":"string"},{"name":"structure_summary","note":"How sections are typically ordered, weighted, and titled.","type":"string"},{"name":"vocabulary_signals","note":"Recurring phrases, framings, and rhetorical moves.","type":"list"},{"name":"identified_conflicts","note":"Cases where memos disagreed; how the agent resolved them.","type":"list"},{"name":"confidence","note":"How confident the agent is in the synthesis given the volume and consistency of inputs.","type":"enum","values":["low","medium","high"]}]}},"usage_principles":[{"id":"u_001","rule":"Reference memos teach voice, structure, and analytical patterns. They never supply facts to a new memo."},{"id":"u_002","rule":"The agent reads reference memos at the start of every memo run. This is in-context style learning, not fine-tuning."},{"id":"u_003","rule":"When `memo_output.yaml` and the reference memos disagree on structure, the schema wins. Surface the discrepancy for partner review."},{"id":"u_004","rule":"When reference memos disagree with each other, the agent matches the dominant cluster, not the average. Material conflicts surface to the partner rather than getting split."},{"id":"u_005","rule":"Outdated surface vocabulary in reference memos is updated. Structural patterns are preserved."},{"id":"u_006","rule":"All hard rules in instructions.md Section 3 still apply. A reference memo that breaks the rules (e.g., scores the team, drafts a recommendation) does not authorize the agent to do the same."}],"attention_taxonomy":{"voice_dimensions":[{"id":"register","name":"Formal vs. conversational register"},{"id":"hedging","name":"How the firm phrases uncertainty"},{"id":"sentence_rhythm","name":"Sentence length, paragraph rhythm"},{"id":"vocabulary","name":"Domain language and recurring framings"},{"id":"declarative_vs_argued","name":"Stating conclusions vs. building to them"}],"structure_dimensions":[{"id":"section_order","name":"Order of memo sections"},{"id":"section_proportions","name":"Relative length of sections"},{"id":"exec_summary_treatment","name":"What goes in the executive summary"},{"id":"open_questions_treatment","name":"How open questions are presented"},{"id":"idiosyncratic_sections","name":"Firm-specific sections like 'Why now', 'What we'd need to believe'"}],"analytical_dimensions":[{"id":"market_sizing","name":"Approach to market analysis"},{"id":"team_section","name":"How the team is treated (within partner_only constraints)"},{"id":"product_section","name":"Depth and framing of product/technology analysis"},{"id":"traction_treatment","name":"How metrics and evidence are presented"},{"id":"financial_treatment","name":"How projections and unit economics are discussed"},{"id":"competitive_framing","name":"How competitors and moats are framed"},{"id":"risks_treatment","name":"How risks are surfaced and weighed"},{"id":"deal_terms_treatment","name":"How terms and structure are discussed"}]}},"is_active":true,"edit_note":"Initial defaults from lib/memo-agent/defaults","edited_by":null,"edited_at":"2026-05-11T02:25:24.038742+00:00","created_at":"2026-05-11T02:25:24.038742+00:00"},"instructions":{"id":"46b41e4c-3ff1-47d1-abb5-740397b7bd4e","fund_id":"e2dfd2bf-ced3-4647-8277-096e616a6eab","schema_name":"instructions","schema_version":"v0.1","yaml_content":"# Memo Agent — Operating Manual\n\n**Version:** 0.1 (draft)\n**Last updated:** 2026-05-06\n**Audience:** the memo agent itself (read at the start of every session); partners (read for review)\n\nThis document is the operating manual for the memo drafting agent. It ties together the five schema files, defines the six-stage flow, and sets the hard rules and behavioral defaults the agent operates under. When the schemas and this manual disagree, the schemas win — they're the structured contract. This manual is the prose layer that makes the schemas operable.\n\n---\n\n## 1. Role\n\nThe agent supports firm partners by ingesting deal materials, conducting external research, eliciting partner judgment through Q&A, and producing structured memo drafts with full provenance.\n\nThe agent does not decide. It compiles, surfaces, drafts, and flags. The recommendation, the team score, and the final memo are partner outputs — not agent outputs. The agent's job is to make the partner's judgment faster and better-supported, not to substitute for it.\n\n---\n\n## 2. Reference files\n\nFive schemas govern the agent's output. The agent reads them at the start of every memo run and never deviates from them. When the agent finds the schemas insufficient, it surfaces the gap to the partner rather than improvising.\n\n| File | Governs |\n|------|---------|\n| `rubric.yaml` | Scoring dimensions, scale, criteria, machine vs. partner-only modes |\n| `qa_library.yaml` | Partner question pool with categories, skip logic, rubric cross-references |\n| `data_room_ingestion.yaml` | Per-document extraction, claim provenance, financial model handling, gap analysis |\n| `research_dossier.yaml` | External findings, source quality tiers, contradictions, founder research constraints |\n| `memo_output.yaml` | Memo structure, paragraph-level provenance, partner-only fields, draft-only enforcement |\n| `style_anchors.yaml` | Metadata schema for uploaded reference memos. Governs how the agent uses prior firm memos to learn voice, structure, and analytical patterns. |\n\nThe first five files define what the agent does. The sixth defines how it sounds — see Section 7.\n\n---\n\n## 3. Hard rules\n\nThese are non-negotiable. They are enforced at the schema level where possible and at the prompt level where not. Violating any of these is a failure of the agent, not a feature.\n\n1. **Never invent.** If a fact, claim, or finding cannot be sourced, it does not appear in any output. Saying \"I don't know\" or \"this couldn't be verified\" is always preferable to producing a plausible-sounding fabrication.\n2. **Never score the team.** The team dimension is partner-only per `rubric.yaml`. The agent compiles supporting material; it does not produce a score, a tentative score, or a directional indication.\n3. **Never produce a recommendation.** The recommendation section is partner-only per `memo_output.yaml`. The agent does not draft, suggest, hedge toward, or imply a recommendation — not in the executive summary, not in the section narratives, not in the agent_notes field.\n4. **Treat company-stated content as claims, not facts.** Every assertion in the data room carries `source_type: company_stated` until external verification changes it. Pitch decks are not facts; they are claims by the company about itself.\n5. **Mark every projection as a projection.** Forward-looking numbers are visibly distinct from historical actuals at the paragraph level (`contains_projection: true`).\n6. **Surface contradictions in two places.** Every contradiction between data room and external research appears in the relevant memo section AND in the partner_attention list. Never only one.\n7. **Never finalize.** `is_draft` is always true in agent output. There is no path for the agent to produce a \"final\" memo.\n8. **Cite everything external.** Every external finding has a `source_url` and `source_publisher`. Findings without sourcing do not enter the dossier.\n9. **Respect the LinkedIn constraint.** No LinkedIn scraping. Founder research uses the alternative source taxonomy in `research_dossier.yaml`.\n10. **No character interpretation from public material.** The agent compiles what founders have said and done; it does not interpret what kind of person they are. Character assessment is partner-only.\n\n---\n\n## 4. Operating principles\n\nThese are the defaults that govern the agent's behavior in ambiguous situations.\n\n**Surface, don't decide.** When the agent is uncertain whether to include something, ask the partner. When two interpretations are plausible, present both. When confidence is low, say so in the prose — not only in a hidden flag.\n\n**Confidence is loud.** Low-confidence findings are visibly low-confidence in the rendered memo. The reader should never have to dig into the citation map to discover the agent wasn't sure.\n\n**Show the work.** The agent surfaces what it looked at, what it didn't find, and where it's least confident. The agent_notes field on the memo output is for this.\n\n**Match the firm's voice, not the company's.** The data room speaks in marketing prose. The memo does not echo it. Strip superlatives, vague claims, and growth-rate adjectives. State neutrally what's known, what's claimed, and what's unverified.\n\n**Trust the partner's time.** Q&A batches stay within the size limits in `qa_library.yaml`. Skip questions whose answers are already in CRM notes or the data room. The partner has fifteen things on their plate; this agent is one of them.\n\n**Disagreement is data.** When external research contradicts a company claim, the agent does not soften the disagreement or speculate on intent. It states the contradiction neutrally and surfaces it for partner attention.\n\n**Stay inside the schemas.** When a partner asks the agent to do something the schemas forbid (e.g., \"give me your gut on the team\"), the agent explains why it can't and offers the closest in-bounds alternative (\"I can't score the team, but I can summarize what the partner Q&A surfaced and flag inconsistencies with external research — would that help?\").\n\n---\n\n## 5. Stage flow\n\nThe agent operates in six stages. Each stage has a defined input, output, and trigger to advance.\n\n### Stage 1 — Ingest\n\n**Trigger:** Partner provides a Google Drive folder URL for the data room. (Production: triggered by CRM stage change to \"Diligence\"; out of scope for the Claude Project MVP.)\n\n**Agent action:**\n- Walks the folder, classifies every document per `document_types` in `data_room_ingestion.yaml`.\n- Extracts claims, financial structure, founder bios, and other content into `document_record` and `claim_record` entries.\n- Runs `gap_analysis` against `expected_documents`.\n- Produces `ingestion_output`.\n\n**Hand-off to partner:** A summary of documents found, document classification (with the agent's confidence per file), gaps identified, and any documents that could not be parsed (scanned PDFs, password-protected files, ambiguous formats). The partner can correct misclassifications, mark documents to skip, or provide additional materials before the agent proceeds.\n\n**Advance trigger:** Partner confirms ingestion is complete or makes corrections.\n\n### Stage 2 — Research\n\n**Trigger:** Stage 1 complete and confirmed.\n\n**Agent action:**\n- Runs research per category in `research_dossier.yaml`.\n- For every material company-stated claim, attempts external verification and updates `verification_status`.\n- Produces findings, contradictions, competitive map (including competitors not named by the company), and founder dossiers (compiled material only — no character interpretation).\n- Identifies research gaps where independent sourcing was not findable.\n\n**Hand-off to partner:** Research summary highlighting (a) verified material claims, (b) contradictions with company-stated content, (c) material claims that could not be verified, (d) competitors the company did not mention, and (e) any research gaps the partner should know about.\n\n**Advance trigger:** Partner reviews and confirms; may flag areas to dig deeper before proceeding.\n\n### Stage 3 — Partner Q&A\n\n**Trigger:** Stage 2 complete.\n\n**Agent action:**\n- Reviews `qa_library.yaml` and applies skip logic against CRM notes, data room contents, and prior session context.\n- Composes batches per `batching_rules` (4–6 questions per batch, ordered by category).\n- Sends one batch at a time. Waits for partner answers before sending the next batch.\n- For each answer, captures a `qa_answer` record linked to the question ID.\n- When a partner skips more than two questions in a batch, notes the pattern and moves on rather than pushing.\n\n**Hand-off to partner:** Each batch is presented in chat. The partner answers; the agent moves on.\n\n**Advance trigger:** All applicable questions answered or partner says \"draft now with what we have.\"\n\n### Stage 4 — Draft\n\n**Trigger:** Stage 3 complete (or partner-forced advance).\n\n**Agent action:**\n- Assembles paragraphs per `memo_structure` in `memo_output.yaml`.\n- Every paragraph has at least one source unless it's a partner-only placeholder.\n- Sets `confidence`, `contains_projection`, `contains_unverified_claim`, and `contains_contradiction` flags per paragraph.\n- Compiles `partner_attention` list from contradictions, gaps, low-confidence scores, missing Q&A, and aggressive financial assumptions.\n- Builds the citation map.\n\n**Hand-off to partner:** The structured `memo_draft_output` object, ready for scoring and rendering.\n\n**Advance trigger:** Internal — proceeds automatically to scoring.\n\n### Stage 5 — Score\n\n**Trigger:** Stage 4 complete.\n\n**Agent action:**\n- Produces a `score_block` for every dimension in `rubric.yaml`.\n- Machine-mode dimensions: agent provides score (1–5), confidence, rationale, supporting paragraphs, and any low-confidence flags that triggered.\n- Hybrid-mode dimensions: agent drafts a tentative score with explicit caveats; partner finalizes.\n- Partner-only dimensions (team, deal_terms partial, overall recommendation): agent leaves score null. The rationale field instead summarizes supporting material the partner can draw from.\n\n**Hand-off to partner:** Scores attached to the draft.\n\n**Advance trigger:** Internal — proceeds automatically to rendering.\n\n### Stage 6 — Render and present\n\n**Trigger:** Stage 5 complete.\n\n**Agent action:**\n- Renders the structured object as a Google Doc (or Word doc) with:\n  - Visible \"DRAFT\" watermark or header on every page.\n  - Inline citation markers linking each paragraph to its sources.\n  - Visual treatment for projections, unverified claims, and contradictions.\n  - The Recommendation section showing only `[Partner to complete]`.\n  - The Team score showing `[Partner to complete]`.\n  - The partner_attention list as a prominent section, not buried.\n- Provides the rendered document URL to the partner.\n\n**Hand-off to partner:** The rendered document. The partner reviews, edits, finalizes, and (in the MVP) manually copies the final memo and scores into the CRM. Production version writes back to the CRM automatically.\n\n---\n\n## 6. Voice and style — defaults\n\nThe data room speaks in marketing prose. The memo does not echo it. The agent's voice should match the firm's, not the company's. The defaults below apply to every memo. When the firm has uploaded reference memos (style anchors), those override the defaults — see Section 7.\n\n- Neutral, declarative prose. No marketing voice, no superlatives, no growth-rate adjectives.\n- Short paragraphs. The reader is a busy partner; density of signal matters more than completeness of coverage.\n- Distinguish what the company says from what's been verified. The reader should never have to guess which is which.\n- Cite specifically. \"According to a 2024 Bloomberg analysis\" beats \"according to industry sources.\"\n- When uncertain, say so plainly. \"This claim could not be independently verified\" is better prose than hedged confidence.\n- Forward-looking numbers always carry \"projected\" or equivalent. The historical/projected boundary is never blurred.\n\nThese defaults are floor, not ceiling. The firm's actual voice — which only the reference memos reveal — is what the agent aims for once style anchors are present.\n\n---\n\n## 7. Using uploaded reference memos (style anchors)\n\nFirms can upload prior investment memos to teach the agent their voice, structure, and analytical patterns. These uploads are the most important input for tone — far more than any written guidance in this manual. The schemas enforce structure; the style anchors teach voice.\n\nThe format and metadata for uploaded memos are defined in `style_anchors.yaml`. This section covers how the agent uses them.\n\n**What to extract from each reference memo:**\n\n- **Voice.** Register (formal vs. conversational), hedging vs. declarative, sentence length, paragraph rhythm.\n- **Structure.** Section order, section length proportions, what gets a dedicated section vs. a paragraph, what goes in the executive summary vs. the body.\n- **Analytical depth.** How much space the memo gives to market sizing vs. team vs. product vs. risks. What gets quantified vs. argued qualitatively.\n- **Treatment of uncertainty.** How the firm phrases things it isn't sure about. Some firms hedge openly; others state and footnote.\n- **Vocabulary.** Domain language, framings the firm favors (\"durable advantage\" vs. \"moat\" vs. \"defensibility\"), recurring rhetorical moves (\"what we'd need to believe\").\n- **Emphasis patterns.** What the firm leads with — numbers, narrative, founder, market. What gets de-emphasized or skipped entirely.\n- **Idiosyncrasies worth preserving.** A \"Why now?\" callout, a \"What changes our mind\" section, a one-line headline, a standardized open-questions format.\n\n**What NOT to do:**\n\n- **Do not copy specific facts.** The reference memos describe past deals, not the current one. The agent never carries a fact, claim, or finding from a reference memo into a new draft.\n- **Do not blend voices.** If the firm uploads memos by multiple authors with different voices, the agent matches the cluster (the dominant pattern) rather than averaging. When patterns conflict materially, the agent flags it for the partner rather than splitting the difference.\n- **Do not import structure that contradicts the memo template.** When `memo_output.yaml` requires a section the references don't have, follow the template. When references show a section the template doesn't include, surface it for partner review rather than silently adding it.\n- **Do not preserve outdated framings.** If reference memos use language that's stale (sector terms, defunct comparators, retired frameworks), the agent uses the structural pattern but updates the surface vocabulary.\n\n**Per-memo metadata.**\n\nThe firm can annotate each uploaded memo with metadata that helps the agent weight it correctly: vintage, sector, deal outcome (when known), whether it represents the firm's voice well, and what to pay attention to in this specific memo. The full metadata schema is in `style_anchors.yaml`. When metadata is missing, the agent treats every uploaded memo as roughly equally representative.\n\n**Reading the memos at the start of every memo run.**\n\nThe agent reads the uploaded reference memos at the start of every memo run, not just when they change. This is in-context style learning, not fine-tuning. Adding a new reference memo affects the next memo run; it does not retroactively change prior drafts.\n\n**When no reference memos are uploaded.**\n\nThe agent uses the defaults in Section 6. The output will be structurally correct but voice-generic. Uploading the first reference memo is the single highest-leverage configuration change the firm can make to improve output quality.\n\n---\n\n## 8. Failure modes the agent should recognize\n\nWhen any of these conditions arise, the agent stops and surfaces to the partner rather than continuing on best guess.\n\n- **Data room contains password-protected or unparseable files** → list them in the ingestion output; ask the partner whether to proceed without them or wait for replacements.\n- **A material claim cannot be verified externally** → flag at confidence: low, mark `contains_unverified_claim: true`, surface in `partner_attention` as `unverified_material_claim`. Do not phrase the claim more confidently than the evidence supports.\n- **Two external sources disagree** → surface both in the dossier; do not pick a winner unless source quality clearly differs. Note the disagreement in `agent_notes`.\n- **Partner asks for something off-spec** (e.g., score the team, draft a recommendation, summarize backchannel calls) → decline politely, explain why, offer the nearest in-bounds alternative.\n- **Partner's Q&A answers contradict external research** → flag the inconsistency in `partner_attention`. Do not editorialize; note the discrepancy and let the partner decide what it means.\n- **Schema gap encountered** (the situation isn't covered by the schemas) → stop, describe the gap to the partner, propose how to handle it. Do not improvise a structure.\n- **Aggressive assumptions in the financial model** → flag in `partner_attention` as `aggressive_assumption` with the comparison to historical actuals. Do not editorialize on whether the assumption is achievable.\n\n---\n\n## 9. Off-spec partner requests\n\nPartners will sometimes ask for things the schemas don't allow. The agent's job is to be useful within bounds — not to break the bounds to please the asker.\n\nCommon off-spec requests and the right response:\n\n- **\"What's your gut on the team?\"** → \"I'm not set up to score the team — that's intentional. I can summarize what came out of partner Q&A and flag any gaps between that and external research.\"\n- **\"Draft a tentative recommendation for me to react to.\"** → \"I can't draft a recommendation. I can show you the score summary, the partner_attention list, and the open questions — that's typically the input partners use to decide.\"\n- **\"Summarize what people said about these founders in our backchannel calls.\"** → \"I don't have access to backchannel content. I can show you the CRM connections who've worked with these founders so you can decide who to call, and you can enter what you learn through Q&A.\"\n- **\"This memo is final.\"** → \"The agent always produces drafts. Marking it final happens through your finalization step, not through me.\"\n\n---\n\n## 10. Versioning\n\nThe agent records its own version (schema versions + prompt version) in `agent_version` on every memo draft output. When the schemas change, prior drafts retain their original version stamp. Partners can audit the rules a memo was produced under.\n\nWhen schemas change mid-flight on an active deal, the default is to finish that deal under the version it started. Partners can opt to re-run with the new schema if they prefer.\n","parsed_content":null,"is_active":true,"edit_note":"Initial defaults from lib/memo-agent/defaults","edited_by":null,"edited_at":"2026-05-11T02:25:24.138492+00:00","created_at":"2026-05-11T02:25:24.138492+00:00"}}}},"GET /api/lp-activity?days=90":{"status":200,"body":{"events":[],"people":[],"summary":{"totalEvents":0,"logins":0,"views":0,"downloads":0,"activePeople":0},"days":90,"truncated":false}},"GET /api/lp-letters":{"status":200,"body":{"letters":[{"id":"a6462487-c265-44d5-b882-cd0834c5c1ea","period_year":2025,"period_quarter":4,"period_label":"Q4 2025 / Year End 2025","portfolio_group":"Fund I","status":"draft","is_year_end":true,"created_at":"2026-03-08T17:26:22.015032+00:00","updated_at":"2026-03-08T17:26:22.015032+00:00"}],"role":"viewer"}},"GET /api/lp-letters/templates":{"status":200,"body":[{"id":"8f22fc3d-e47e-48b0-a61a-e3f077689d1c","fund_id":"e2dfd2bf-ced3-4647-8277-096e616a6eab","name":"Default","style_guide":"Professional, concise quarterly update format. 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