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datatruth Get your Truth Score

Private equity · Growth equity

An AI thesis on paper isn’t an AI thesis in the data.

Growth cases increasingly price in AI: churn prediction, lead scoring, pricing. A fast-growing company can outrun its systems, leaving the data those plays need split across a CRM, a product database and billing that never agreed. We test the plays in the thesis against the company’s records before you price them, and again before you sell.

Test one portfolio company Where we fit

Starts with a 30-minute call. Commits you to nothing.

Illustrative example, not a client.

Real sample

See the memo a portfolio company would receive. A full Truth Score Read on 72,837 records in 7 exports from CRM, finance and HR. It scored 59 out of 100, Band C, significant work needed: 823 finance customers with no CRM account, and only 13% of deals reconcile to invoices.

Read the sample memo

Real engine output on generated test data, not a client.

The challenge

Growth outruns the systems underneath it.

A company growing quickly adds customers faster than it tidies records. The CRM, the product database and billing each keep their own version of the customer, and the AI upside in the model assumes they agree.

When they don’t, the plays stall, the growth story softens, and a buyer’s diligence finds the gap before you do. Testing the thesis play by play turns an assumption into evidence, whichever way it falls.

If the data can’t carry it, the AI upside is a slide, not a plan.

How to read the card above: a Truth Score™ is a score out of 100 for how far the data can be trusted. Beside it is a Leverability read (which AI plays the data can run now, after fixes, or not at all; nothing to do with debt). Each play sits next to the target in the plan that leans on it.

Where we fit

From the growth thesis to the exit story.

  1. 01At entry

    The thesis, tested

    A Truth Score Read on the AI plays in the growth case, from exports the company or the data room provides.

    Truth Score ReadMore

  2. 02In the plan

    What to deploy, what to fix

    Every AI bet ranked, with a roadmap management can hand to its own team or any outside firm.

    AI Investment ReviewMore

  3. 03Before the exit

    The story, re-read

    A read on the systems the buyer’s diligence will open, so the gaps they would find are found, fixed or explained before the data room opens.

    Truth Score ReadMore

Questions

Asked plainly.

What investors in this strategy ask before a portfolio company sends a single export.

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We’re a minority investor. Can we still use this?

Yes. With a minority position the company’s leadership can commission the work and share the result. We write the memo for management, and it answers the board’s questions too.

Can you test the thesis before we price it?

Yes, from data-room exports with the seller’s consent, or from exports the company shares directly under NDA. See data diligence.

Do you compare the company against other growth companies?

No. We don’t publish benchmarks we don’t have. The read compares the company’s own systems against each other, and against the plays in your thesis.

What does it cost?

A fixed fee, agreed before we start, that never moves with the verdict. It is a small fraction of the proposal it tests; if it wouldn’t be, we’ll tell you on the call. Our first clients get a founding rate in exchange for a private reference call once they’re happy with the work, only with their consent. We never name a client, fund or target publicly without written consent.

Other strategies

The same method, pointed elsewhere.

Put evidence under the AI upside.

Send the thesis and exports from two systems. Each AI play tested, within 5 working days of complete exports.

Test one portfolio company See a real sample memo

Starts with a 30-minute call. Commits you to nothing.

Get your Truth Score