Industries · Last updated Sept 2026

AI for private equity that reads the whole dataroom.

Your associates spend diligence weeks extracting numbers from PDFs and building the same models from scratch. We build AI that reads the full dataroom, drafts the analysis, and flags what a human needs to check.

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A closer look

Work shaped around ai for private equity

We've cut a diligence workflow from 3 months to 2 weeks. Our public library ships free runnable versions of LBO diligence, private credit underwriting, and portfolio monitoring stacks. Run one on a live deal before you pay anyone.

Where we help

AI workflows for ai for private equity

Due diligence automation across financial, commercial, and legal workstreams

Dataroom ingestion and structured extraction

LBO and returns model drafting from target financials

Portfolio company monitoring and board pack analysis

IC memo drafting grounded in source documents

The intended difference

Operational value

Diligence in weeks, not months
Every portfolio board pack read, every quarter
Associates on judgment work, not extraction

Inside the work

Where the hours actually go in a deal

Most diligence time isn't analysis. It's locating the number, checking it against another document, and formatting it for the model. A dataroom with 400 files gets skimmed, not read, because no team has the hours. AI changes the economics: every file gets read, every inconsistency between the CIM and the underlying statements gets flagged, and the associate starts from a drafted workbook instead of a blank one.

The same applies post-close. Portfolio monitoring usually means a quarterly skim of board packs across a dozen companies. A monitoring pipeline reads every pack the day it lands, tracks covenant headroom and KPI drift, and writes a one-page exception note per company. Partners read exceptions, not packs.

Inside the work

Try the free versions before buying anything

Our public build library includes free, runnable versions of the exact stacks we sell to funds: an LBO diligence kill sheet, a private credit underwriting pack, a portfolio monitoring pack, a VC and startup diligence pack, and a returns model. They run on your own Claude account on your own machine.

Run one on a live deal this week. If it moves the needle, the paid engagement is wiring it into your stack properly, with the controls and data plumbing a fund actually needs. If it doesn't, you've spent nothing and learned where the models stand.

A few useful details

Questions about ai for private equity

How is AI used in private equity?

Four places today: diligence document review and extraction, model drafting from target financials, portfolio monitoring across board packs and management reports, and first-draft IC memos. The fund keeps the judgment. AI does the reading and drafting at scale.

Can AI really speed up due diligence?

Yes, materially. We cut one diligence workflow from 3 months to 2 weeks by having AI read the dataroom, extract the numbers, and draft the analysis while humans verified flagged items. The bottleneck moved from reading speed to decision speed.

Is a dataroom safe to run through AI?

With the right setup, yes. We build under NDA with locked-down environments, no training on your data, and fully local pipelines where documents never leave your machines. Access logs show exactly what was read and by which process.

What about hallucinated numbers in diligence?

Every extracted figure carries a link back to its source page, and material numbers get re-derived by an independent pass that must agree before the figure survives. Anything unverified is marked as such. A human clears every flag before the memo moves.

Do you work with mid-market funds?

Yes, that's the sweet spot. Mid-market funds feel the diligence squeeze hardest: lean deal teams, more deals screened, no internal data science group. A fixed-scope build that reads datarooms pays for itself inside one deal cycle.

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