I built an AI investment memo generator for a fund. The honest result, up front: it turns a data room, a deck, and call notes into a clean, structured first-draft memo - and left unsupervised, it will state a confident ownership percentage the cap table never supports. Both are true at once. One is the pitch. The other is why a partner still signs the memo, not the model.
This is an anonymized account of a real build. I never name the funds I work with, so take the fund here as a multi-stage shop with the problem every fund has: too many deals, a memo between each one and a check, and not enough analyst hours to write them all well.
IWhat We Actually Built
The workflow is the same everywhere. Analysts and associates write the first memo; partners edit it and own the call. The fund wanted a tool that removed the blank page without removing the judgment - something that reads the raw inputs of a deal and hands back a structured first draft mapped to their own template, not a generic one off the internet.
So the generator ingests three things - the data room, the founder's deck, and the call notes - and returns a first-draft memo, section by section, with every claim tied back to the document it came from. It is deliberately not a decision engine. The analyst still reads it, argues with it, and rewrites the half that matters.
This is the same shape as everything else I build. Pitch Deck Scanner routes decks into the CRM; how I built GoBriefed turns a calendar into pre-meeting briefs; a board-deck scanner turns board packs into KPIs. Extract, structure, human override. The memo generator is that pattern pointed at the highest-stakes document a fund produces - which is why the guardrails matter more here. None of it is science fiction: Gartner forecast in 2021 that by 2025 more than 75% of venture and early-stage investor reviews would be informed by AI and data analytics.
IIThe Memo Structure We Built It Around
You cannot generate a good memo until you agree on what a memo is. Carta's structure is the one most funds recognize: executive summary and thesis, market opportunity (TAM, SAM, SOM), product, team, traction and financials, competitive landscape, risks and mitigations, and deal terms and valuation. A model given a clear structure to fill behaves far better than one asked to decide what a memo should contain.
The fuller investment-committee version runs longer - closer to 11 sections. VC Beast frames the IC memo as 'a structured argument - part analysis, part conviction, part risk management - that answers one central question: why should this fund invest in this company, at this price, right now.' The model can own the analysis scaffolding. It cannot own the conviction, and the design never asks it to.
Worth naming the gap: the pages that rank for memo queries are static. Bessemer's public memo library sits at the top of results for 'investment memo template.' All good to read - but a template shows you what good looks like; it does nothing to get you from a 200-page data room to a first draft.
aWhy length has to match the deal stage
Rigor scales with stage. Venture Capital Careers lays out the ladder: a screening note is a page or less, a case-study memo one to three pages, a diligence memo two to five, and a full IC memo five to fifteen pages plus appendices. The roles are fixed - analysts and associates draft, principals and partners edit and own the recommendation. So the generator takes the stage as an input and drafts to match. It drafts at the right length for the stage. It does not decide what the stage should be.
IIIHow the Pipeline Is Wired
The generator runs in three passes. The first is structuring: it reads the data room, deck, and notes and pulls every usable fact into the right section - revenue into traction, backgrounds into team, round mechanics into deal terms - tagging each with its source page. Nothing is written yet. The second is drafting: it writes each section from those structured facts, and only those facts, so the model is phrasing something it already holds rather than improvising from memory. The third is a consistency check: it reads the whole memo back and flags where sections disagree - where the market size in the summary does not match the market section, where the ownership figure does not match the cap-table section.
That third pass is the part most build guides skip, and the part that matters most. A memo is a single argument; it cannot carry two different numbers for the same fact and still be trusted.
aWhy we didn't split it into four parallel, independent drafts
The popular way to build one is to fan the work out. Stack AI's build feeds the knowledge base into four parallel model instances - one each for Business Overview, Competitive Landscape, Financial Overview, and Executive Summary - then stitches them together. It is fast, and it is a trap. Four models that never read each other's work produce four confident sections that quietly contradict: the summary claims a $4B market; the market section computes $900M; nobody caught it because nobody held both pages at once. A memo whose sections disagree is worse than no memo - an IC member only has to catch one seam to stop trusting the rest. One structured pass plus a consistency check costs a little speed and buys back exactly that failure.
IVWhere It Nails the Structure
Here is what genuinely works. The parts of a memo that are pure structure - restating the thesis, laying out TAM, SAM, and SOM, organizing traction, formatting the competitive landscape into a grid - the model does fast, consistently, and without complaint. It produces a clean first draft of the skeleton every time, and never gets bored on the tenth deal of the week the way a tired associate does.
That is worth more than it sounds. Venture investors spend, on average, 118 hours scrutinizing the facts before investing, and reject roughly 100 ideas for every one they fund. The memo, as that piece puts it, is almost the exact opposite of a sales pitch - its job is to find the weak spots early. The generator does not do that scrutiny for you. It clears the scaffolding so you reach the weak-spot hunting faster, on more deals. The hours you save are the low-judgment hours. The hours you keep were always the point.
VThe Honest Limit: Memo-Writing Was Never the Real Bottleneck
Now the part the sales deck leaves out. Memo drafting is not where the hours go. The Prodzen, across 50-plus calls with VC and PE teams, estimates that 30 to 40% of investment-team hours disappear into logging, chasing, and reformatting - not writing the IC memo. V7 Labs, directionally, puts 15 to 25 hours of a senior associate's time into gathering and structuring data before drafting even begins.
So a generator that only writes the memo speeds up the smaller slice. I say that plainly, because pretending otherwise is how these tools lose trust. The version we built leans hard into the input side - the extraction and structuring - because that is where the hours sit. The drafting is the easy mile.
VIWhere It Breaks
Three failure modes showed up in the build. Each has a fix, and two come down to which model reads the data room. The benchmarks are not academic - they map onto whether a number in your memo is real.
aThe fully-diluted ownership trap
The quietest, most expensive error is an ownership number that is not fully diluted. Venture Capital Careers names it exactly: report ownership without calculating it on a fully diluted basis, option pool included, and an IC member who sees 15% and later learns it was 12% fully diluted stops trusting the rest of the memo. An AI drafting layer introduces this silently if you let it restate a bare percentage from one slide. The fix: forbid the bare percentage. The model shows the cap-table math inline - pre-money, round size, option pool, resulting fully diluted stake - or flags that it cannot. No arithmetic, no number.
bNumbers the data room never supplied
The second failure is invention. The FAITH benchmark, presented at ICAIF 2025, masks spans inside real S&P 500 10-K tables precisely because financial applications demand trustworthy numerical reasoning - and found leading models still mishandle values, units, and cross-table calculations on real filings. It gets worse when the model simply does not know. On Artificial Analysis's AA-Omniscience benchmark, GPT-5.5 fabricates a confident answer 86% of the time it does not know, against 35.9% for Claude Opus 4.8. The guardrail: every figure cites its source page or gets flagged as unverified. The model is never allowed to fill a gap. If the data room did not supply it, the memo says so.
cFinding the needle in a 200-page data room
A real data room is hundreds of pages across dozens of documents, and the fact you need is buried in one of them. That is a long-context retrieval problem, and the models are not equal at it. On GraphWalks BFS at one million tokens, Claude Opus 4.8 scores 68.1% against GPT-5.5's 45.4% - a 22.7-point gap reviewers call architecturally decisive for work that reasons over whole multi-document sets. That is why model choice matters when the tool reads the full data room instead of a tidy excerpt. I went deeper in ChatGPT vs. Claude for VC due diligence - the short version is that the model doing the reading is not interchangeable.
VIIWhat Still Needs a Partner
Everything above is extraction and structure. None of it is the decision. The judgment calls - framing the thesis, weighing the bear case, deciding which risk is disqualifying versus survivable, making the price call - stay with a human, and the honest vendors agree. V7 Labs concedes that no single tool generates a publication-ready IC memo from a data room upload - the tools automate parts: extracting, synthesising, drafting. Flybridge's open-sourced generator is just as plain, describing its output as covering 50 to 60% of the work and requiring human input for nuance and judgment.
VIIIThe Honest Verdict
An AI investment memo generator is a drafting accelerant for the skeleton of the memo. It is not a replacement for the 118 hours of scrutiny, and it is not the partner's signature on the recommendation. Build it to show its math, cite every number, and flag what it cannot verify, and it earns a permanent place in the workflow. Build it to spin confident prose out of a data-room upload with no guardrails, and it will eventually hand a partner a number that does not exist.
That is the same build-and-operate pattern behind everything we ship - including a $200M+ climate-tech fund's LP-grade portfolio platform: built on the fund's own stack, run as part of the workflow, never sold as a black box. Black Matter is an AI systems studio and consultancy - we build these tools for funds and then operate them. If you want one pointed at your data room, your template, and your CRM, that is what the retainer is for.
IXFAQ
Can this run on our own data room and CRM?
Yes. It is built on your stack - your memo template, your data room, your CRM - not a separate piece of SaaS you log into. The whole point of the build-and-operate model is that it fits the workflow you already have.
Does it replace an analyst's first draft?
No. It produces the first draft the analyst edits. It clears the blank page and the formatting grind so your team spends its hours on scrutiny and judgment, not scaffolding. It is an accelerant, not a decision-maker.
What happens when it isn't sure about a number?
It flags the figure as unverified and refuses to fill the gap. Every number that makes the draft cites its source page, and ownership percentages have to show their cap-table math or they do not ship.
How is this different from Pitch Deck Scanner or GoBriefed?
Same shape - extract, structure, human override - pointed at a different document. Pitch Deck Scanner handles decks into the CRM, GoBriefed handles pre-meeting briefs, and this handles the first-draft memo. One pattern, built for the highest-stakes document in the pipeline.
— Michael Rouveure
