In a deal, the same assessment points both ways. A buyer prices AI exposure before paying for it; a seller evidences the moat before the market discounts it. Same ledger, same scale, both doors.
Every buyer of a software or services company now applies a silent discount for AI-replication risk. An exposure assessment replaces the silence with a number: which positions the target's earnings stand on, and how each scores on the M-scale.
The assessment runs on public materials and requires no cooperation from the target — which means it works pre-LOI, on shortlists, and on live deals where the seller does not know. One question tests whether you need it: would replication evidence have changed the price on your last deal?
| Position | Score |
|---|---|
| Core product | M4 |
| Structured customer data | M1 |
| Onboarding services | M3 |
Founders routinely misidentify their own moat. They believe it is the code; the assessment shows it is the seven years of structured customer data, the workflow lock-in, or the certification stack. That finding is an equity story — the weakness a buyer would attack, restated as the precise location of value.
Written into the information memorandum with an AI plan attached, it converts the discount every buyer silently applies into a priced upside. Findings unfavourable to the seller are reported unchanged; that is what makes the favourable ones worth something.
The moat does not sit in the product code. It sits in three defended positions: the structured claims archive (M1), the certification stack (M1), and the payer integrations (M2). The equity story should be built on these — not on features a competent team can replicate in a quarter.
Three engagements, one methodology. Each produces a dated, scored document the other side's advisors can rely on — because the method is built to survive their lawyers attacking it in a price negotiation.
A conclusion that cannot survive the other side's advisors is worth nothing in a negotiation. The protocol is versioned, so results are comparable across engagements — and every finding carries its evidence.