AI for venture capital, from the inbound deck to the quarterly LP letter

Your associates read decks at night, chase founders for monthly numbers and rebuild the LP letter by hand every quarter. We build AI agents that screen each deck against your thesis, keep your CRM current, collect founder updates into one KPI table and draft the letter from figures your database holds. They run in your own cloud, and a partner approves anything that reaches a founder or an LP.

Book a Discovery Call Start with an AI Audit

Jason, our COO, runs the call and scopes the first workflow with you.

Agent run: inbound deck screenSample

Deck receivedSeries A deck through your website form, 18 slides
Done
Thesis checkB2B infrastructure, Series A, US or Europe: 3 of 3 criteria met
Done
Founders and market researchedBoth founders' prior companies and four competitors, each with a source
Done
CRM checkedA portfolio CEO introduced one founder to your team in March
Warm intro
Partner reviewTwo-page screening memo with nine citations
Waiting
Logged to the CRMDeal record, stage, memo and intro path
After approval

What the agent can seeThe deck, the sources you allow and your CRM. Fund records stay out of scope.

Deck and attachments: Visible
Public founder and company sources: Visible
CRM deal and contact records: Read only
LP records and fund financials: Not retrieved
Other companies' data rooms: Not retrieved
Model training on your files: Never

Illustrative run. Thesis criteria, sources and reviewers are set per fund during scoping. The agent never replies to a founder on its own.

Clients include

Where a venture firm's hours go, deal by deal

101

opportunities considered, on average, for each deal a venture firm closes

Source: Gompers, Gornall, Kaplan and Strebulaev, survey of 885 venture capitalists at 681 firms, published in Harvard Business Review (2021)

Of those 101, 28 got a meeting with management and 4.8 reached due diligence. Each figure below maps to the agent that takes on the reading behind it.

22 hours a week on networking and sourcing

Out of a 55-hour week. The screening agent takes the first read of every inbound deck, so sourcing hours go to the founders who fit your thesis.

118 hours of due diligence on each deal that closed

A typical deal took 83 days to close. The diligence agent checks the deck's numbers against the source files, so your team spends those hours on reference calls and the founders.

18 hours a week working with portfolio companies

The update agent collects each company's monthly numbers into one table and flags what moved, so partners spend that time on the companies that need help.

Only 10% of deals came from cold email pitches

More than 30% came from former colleagues and 20% from other investors. An agent that logs every intro in your CRM shows who sent which deal, and the AI Audit ranks which of these workflows to build first.

Source: Paul Gompers, Will Gornall, Steven N. Kaplan and Ilya A. Strebulaev, "How Venture Capitalists Make Decisions", Harvard Business Review, March to April 2021. Survey of 885 institutional venture capitalists at 681 firms, run November 2015 to March 2016; full paper NBER Working Paper 22587.

AI use cases for venture capital firms, from deal flow to LP reporting

Six workflows where your associates and platform team get hours back. In each one the agent reads and drafts, and a partner or associate approves what it produces.

Deal flow

Inbound deck screening against your thesis

The agent reads every deck that reaches your inbox or website form, scores it against your stage, sector, geography and check size, and drafts a one-page screen for the partner who covers that sector.

How your thesis becomes rules an agent follows

Research

Founder and market memos with citations

For each company you take a meeting with, the agent drafts a memo on the founders' track record, the market and the competitors, with a source on every claim and a second agent checking each one.

How second-agent checks and approvals work

Diligence

Startup due diligence, file by file

The agent reads the data room, ties the deck's revenue and retention figures to the billing export, and checks the cap table against the signed SAFEs and option grants. Every finding names the file it came from.

Same document pipeline as our invoice automation

Portfolio

Founder update collection and KPI tracking

Founders keep sending updates in whatever format they use. The agent reads each one, extracts revenue, burn, runway and headcount into one table, and chases the companies that have not reported.

Price the hours spent chasing updates

LP reporting

Quarterly letters and LP reports

The agent drafts each portfolio paragraph of the quarterly letter from the KPI table and last quarter's letter. The figures come from database views, and a partner edits and signs before anything goes to an LP.

Prove it on last quarter's letter first

CRM

CRM hygiene and intro tracking

The agent logs meetings, emails and intros to the right company and contact in Affinity, HubSpot or Salesforce, merges duplicates, and keeps each deal's stage current so pipeline reviews start from clean data.

How agents connect to your CRM and data

AI for due diligence on startups, checked against the source files

A Series A deck states ARR, retention, customer count and the option pool. The agent works through your diligence checklist and checks each one against the file it should come from, so your team starts from the differences.

Diligence check: Series A, Company KSample

Claim in the deckChecked againstResult

ARR of $2.4MBilling export, last 12 monthsMatches

Net revenue retention of 128%Cohort sheet in the data room119% on source dataDeck counts an upsell from a customer acquired in the period

140 paying customersCRM export and billing exportMatches

12% option poolCap table, board consents and grant lettersMatches

Cap table completeSigned SAFEs in the data room1 SAFE missingA $150K SAFE from 2024 is not on the cap table

Illustrative. The checklist, sources and who reviews each finding are set per fund during scoping.

Sources

Every finding names its file

The agent returns the file and page behind each check, and a second agent re-reads that source before the finding reaches your deal lead.

The same second-agent check in our chatbot builds

One company

One workspace per company

Each company's data room runs in its own workspace with its own access list, so a founder's files never sit in another company's context, and files are archived or deleted on the terms your NDA sets.

See the context layer

Your judgment

The agent checks, your partners decide

Team, market and timing stay with your partners. The agent removes the hours spent finding a missing SAFE or a retention figure that does not tie, and hands your team a list of questions for the founders.

See how we scope AI consulting work

Portfolio updates and LP reporting, from founder email to quarterly letter

VC portfolio management runs on updates in a dozen formats: an email, a PDF deck, a spreadsheet, a board pack. The agent reads all of them, builds one table, and drafts the letter your LPs read from that table.

Quarter close: Fund II, Q3Sample

Update inKPIs extractedAgainst last quarterCompany AEmailDoneIn rangeCompany BPDFDoneIn rangeCompany CChasedDoneIn rangeCompany DSheetDoneBurn up 38%Company EBoard packDoneIn rangeCompany FNot in yetWaitingWaiting

Illustrative. KPIs, ranges, reminder wording and recipients are set per fund during scoping.

Letter draft for partner review

Company D grew revenue 22% in the quarter and added 11 people ahead of its Series B. Monthly burn rose to $410K, which leaves 19 months of runway on current cash.

Highlighted figures come from database views over the founders' updates. The model writes the sentence around them, and a verification step names any figure it was not given.

Partner edits
Partner signs
Sent to LPs

Collection

Founders send what they already send

Your founders get no new portal to log into. The agent reads updates from email, shared folders or your portfolio tool, and sends a reminder in your wording when one is late.

Built on self-hosted n8n

Numbers

The maths lives in the database

Growth, burn and runway are calculated in database views, never by the model. The model receives finished numbers and writes the sentence around them.

What agents need from your data

Approval

A partner signs every letter

Nothing reaches an LP without a named partner's approval, and every draft keeps a record of which update each figure came from, so the question "where did this number come from" has an answer.

See AI governance

Founder and LP data stays inside your fund's environment

Founders share their numbers under NDA, and your LPs expect their records kept apart. We draw the boundary first and build the agents inside it.

Your environment

SourcesDecks, data rooms, founder updates, CRMRead through the inboxes, folders and APIs you already use
Context layerScopes each taskA screen sees the deck, a letter sees the KPI table, and neither sees LP records
AgentsSelf-hosted n8nRuns the workflow and its rules in your cloud
ApprovalPartner sign-offBefore anything reaches a founder, the IC or an LP

run 3118 · Q3 letter, Fund II · 14 updates read · 1 chased · 0 unverified figures · approved by partner · sent to LP portal

Outside the boundary

A model provider receives only the passages a task needs, under your own agreement with that provider, or is replaced by a model you host.

Never allowed

Founder or LP files used to train models
A message to a founder or LP without approval
Figures the model calculated itself

Approval gates

A named partner on every outbound message

Pass notes to founders, IC memos and LP letters wait for a named approver. Each workflow has an owner and an escalation path before it goes live.

See enterprise AI governance

Your cloud

Deployed in your AWS, Azure or GCP account

We deploy on self-hosted n8n behind your own auth, with permissions inherited from the systems the agent reads. Every run is logged with what it read, what it changed and who approved it.

See n8n for enterprise

Ask the fund

Answers from your own memos and notes

Once the context layer holds your past memos and partner notes, a chatbot can answer "did we look at this company before, and why did we pass" with the source attached.

How to build a knowledge base chatbot people use

How a venture capital AI build runs

The same team carries your workflow from the first call to the system running in production, and every build is tested in staging before it goes live. If you want proof first, an AI pilot builds one workflow on your data in 2 to 6 weeks for $35,000 to $75,000, and its fee is credited toward the build. See how we work

Jason, COO
AI Audit, if you need one
Eight phases across strategy, operations, data and systems over 4 to 8 weeks, ending in a ranked 90-day roadmap. The fee is credited toward your first build.
Jason, COO
Scope
One workflow, the systems it touches and what it will take, agreed before you commit. Owen joins the first calls until you are onboarded.
- Your thesis, checklist and KPI definitions, written down
- The messages that always go to a partner
- The timeline, before you sign
Maciej, CTO
Build
Built against your real data, in your environment, as production software from the first week.
- Staging first, on last quarter's updates or decks you already passed on
- Your associates test it on real deals before go-live
- Self-hosted n8n in your cloud
Jason, COO
Integrate
Into production behind your auth, your governance and your monitoring, with your team trained on it.
- Connected to your CRM and inboxes
- Every run logged with what it read and who approved it
- Your team trained through our AI training programs
Maciej, CTO
Run and improve
Watched, fixed and extended in weekly sprints by the engineers who built it, through our AI managed services.
- Error alerts to the engineers who built it
- Weekly sprints that you direct
- The next workflow, or a build inside a portfolio company, scoped with you

What your fund gets from a build with us

We build the agents around your thesis and your reporting calendar, and run them in your environment.

	What you get
You receive	Working agents inside your CRM, inboxes, data rooms and LP reporting
Where founder and LP data lives	In your cloud, on self-hosted infrastructure
Fit to your fund	Built around your thesis, diligence checklist and KPI definitions
Who approves	A named partner on every message to a founder, the IC or an LP
Timeline	Scoped per workflow and agreed before you sign
After launch	The engineers who built it monitor and extend it

What venture capital firms ask about AI

Ask us something else

How is AI used in venture capital?

The workflows we build for AI in venture capital cover six jobs: screening inbound decks against a thesis, drafting founder and market memos with sources, checking a startup's numbers in diligence, collecting portfolio updates, drafting LP letters, and keeping the CRM current. Each is reading and drafting work with a partner or associate approving the result. Picking which company to back stays with your partners.

Can AI screen pitch decks against our investment thesis?

Yes, once the thesis is written down as criteria: stage, sector, geography, check size and the signals your partners look for. The agent scores each deck against those criteria, says which ones it met and missed, and drafts a screen for the partner who covers the sector. A partner decides who gets a meeting, and any pass note waits for approval.

How does AI for due diligence work on an early-stage startup?

The agent takes the claims in the deck and checks each one against the data room: revenue against the billing export, retention against the cohort data, customer count against the CRM, and the cap table against the signed SAFEs and option grants. Every finding names the file it came from, and a second agent re-reads the source before your deal lead sees it. Reference calls and the judgment on the team stay with your people.

Do we need new LP reporting software?

No. We build on the tools you already use, such as your fund administrator's reports, your portfolio spreadsheets and your LP portal. The agent collects founder updates into one table, calculates the figures in database views and drafts the letter. If you already license portfolio or LP reporting software, the agent reads from it and writes to it through its API.

Does it work with Affinity, HubSpot or our own CRM?

We connect through whatever the CRM exposes, usually its API, and the agent logs meetings, emails and intros to the right records. Affinity, HubSpot and Salesforce all have APIs. For a homegrown tracker in a spreadsheet or Airtable, the agent reads and writes that directly. The AI Audit confirms what each of your systems allows before anything is quoted.

Will AI replace venture capital associates?

No. In our builds the agent reads, extracts and drafts, and associates review the screens and memos instead of writing every first draft. Associates spend more of the week meeting founders and doing reference calls. Every message to a founder, the investment committee or an LP keeps a named person on the approval gate.

What does a first build cost a venture fund?

Each workflow is priced in a written scope before you sign. If you want proof on your own data first, an AI pilot takes 2 to 6 weeks and costs $35,000 to $75,000, with the fee credited toward the build. If you have several candidates, the AI Audit ranks them over 4 to 8 weeks for $10,000 to $100,000, and its fee is credited toward your first build.

Can you build for our portfolio companies too?

Yes. The same team builds inside the companies you back, on their systems, and runs the system after launch. AI for SaaS companies covers the support, renewal and security review workflows we build for software teams. If a portfolio company already runs automations another team built, we can take them over after a technical assessment through AI managed services.

Where should a venture firm start with AI?

With the workflow that eats the most associate hours and has the clearest rules, usually inbound deck screening or quarterly update collection. If you know which one, book a discovery call and Jason scopes it with you. If you want the whole firm mapped first, start with the AI readiness assessment or an enterprise AI roadmap.

Related industries

These pages cover the firms next to yours in the deal chain and the companies you back.

Buyouts

AI in private equity

Deal screening, data room review with page citations and portfolio KPI packs, in your own cloud.

See AI in private equity

Advisory

AI in investment banking

Comps spreading, CIM drafts and model checks for M&A deal teams, each figure traced to its source page.

See AI in investment banking

Portfolio

AI for SaaS companies

Ticket triage, churn signals, renewal prep and security questionnaire answers for your software portfolio companies.

See AI for SaaS companies

Finance

AI in accounting

Bank reconciliations, close files, tax workpapers and client documents for accounting firms, with an accountant signing off.

See AI in accounting

Plan the first build before you book

Map your candidate workflows on the AI use case canvas, price the hours with the automation ROI calculator, or read build vs buy for enterprise AI before you choose.

Decide

AI Audit

Eight phases across strategy, operations, data and systems, ending in a ranked 90-day roadmap.

See the AI Audit

Prove

AI pilot

One workflow built on your own decks or updates in 2 to 6 weeks, before you commit to the full build.

See the AI pilot

Read

Why most AI pilots stall

What separates a proof of concept that reaches production from one that stays a demo.

Read the article

Put your first workflow on the board

Answer a few questions about your team and what you want automated, then Jason scopes it with you on the call.

Owen, Maciej, Jason, Oskar and Ben