Most sales don't fall apart at the negotiating table. They erode quietly in due diligence — the three-to-five-month stretch after the letter of intent when the buyer's accountants, lawyers and operators take the business apart. Every surprise they find becomes leverage: a lower price, a bigger escrow, a longer earnout, or a walked deal. And because you signed exclusivity at the LOI, your other buyers are gone precisely when the pressure starts.
The owners who come through diligence with their price intact are the ones with nothing left to find. That is a preparation problem, and it is exactly the kind of exhaustive, unglamorous work AI is built for.
The Buyer Simulation is a named capability of our engine: before any buyer is approached, the AI takes the facts of your business and works them from the other side of the table. It asks what a strategic acquirer would probe, what a private equity firm's model would flag, where a lender's credit committee would balk. It pushes on the same pressure points a real diligence team will — revenue quality, customer concentration, add-backs, contracts, the owner's role — and reports what it finds as a list of exposures, each with what it would likely cost you and what it would take to fix. The technology page shows where the simulation sits in the full engine.
They are rarely exotic. A serious buyer wants to know: Can every number in your financials be traced to evidence? Would the largest customers stay after you leave? Which contracts actually transfer to a new owner, and which need someone's consent? What happens to the business in the ninety days after you stop answering the phone? Which add-backs to earnings are documented, and which are folklore? The questions are predictable. What varies is whether the answers are ready — and whether the seller learned them before or after signing exclusivity.
Every one of these is discoverable months in advance. None of them improves by being discovered late.
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The economics of early fixes are lopsided. Documenting add-backs before market costs some effort; losing them in diligence costs a multiple of their value. Diversifying revenue or contracting key customers takes months; conceding an earnout because of concentration can put a piece of your price at someone else's mercy for years. The simulation's findings become the pre-market work plan — and whatever cannot be fixed in time gets disclosed and framed on your terms, which is a far stronger position than having it discovered.
The AI finds and tracks the issues; it doesn't decide what to do about them. A senior principal from our deal team goes through the findings with you, separates what matters from what doesn't, and handles the conversations with buyers when the time comes. Nothing is fixed, disclosed or shared without your written go-ahead. It is a different division of labor than the traditional model — the AI vs. Broker page draws the comparison honestly — and there is no retainer or listing fee behind any of it: one success fee at close, and nothing if it doesn't close.
A re-trade is when a buyer lowers the price or worsens the terms after the letter of intent is signed, usually justified by something found in due diligence. Because exclusivity has cut off your other options by then, re-trades are hard to resist. The strongest protection is removing the ammunition before the buyer ever sees the business.
No. Buyers will still run their own diligence with their own advisors, and you will still want yours. What the AI changes is the starting position: it finds the issues a buyer's team will find, months earlier, while there is still time to fix or document them — so diligence confirms your story instead of undermining it.