Cooperative negotiation is a solvable problem.

Daniel ran out of money six months in and took a delivery job to keep the lights on. He still bakes every morning at 5am — but for the last 18 months, Maya has run everything else. The staff. The suppliers. The books. The Instagram that took them from 400 to 11,000 followers.

An investor now wants 20% for $80k. He won't wire the money until Maya and Daniel clarify who owns what. Maya thinks 70/30 is fair. Daniel thinks a handshake is a handshake. They can't agree. They're about to lose the deal.

Maya and Daniel each walked through their side with Mediator privately. Then the engine got to work — drafting candidate agreements, pitting them against each other, scoring each against both sides' needs, round after round, until no new draft could do better.

What it surfaced was something neither Maya nor Daniel had proposed. And something neither would walk away from:

The split: 60/40 — with a way back.

Daniel can restore his 10% either by returning to full-time for six months, or by forgoing $24k in distributions over two years. It's not permanent, it's not punitive, and it ties ownership to what happens next — not to a painful audit of the past.

That's what makes the deal work. Daniel isn't being pushed out; he's being given a choice. Maya isn't absorbing future resentment; she's acknowledging reality.

The agreement also added a management salary so Maya's extra hours are paid in cash, not equity. A clause waiving all claims on the first 18 months. A shotgun buy-sell if it ever falls apart.

Read the full agreement → · See both sides' positions

Each party describes their situation privately — what they need, what they can't give up, what happens if no deal is reached. Neither sees the other's account.

The engine then generates candidate agreements, ranks them pairwise against both sides' stated needs, and iterates on the winners. The scoring is built on the Nash Bargaining Solution — the fair-compromise theorem for which John Nash won the Nobel Prize.

What comes out isn't an "AI opinion." It's the agreement that survives a tournament of alternatives, measured against both sets of interests simultaneously. Computers can evaluate tradeoffs humans approximate poorly — especially when both sides are dug in.