Show HN: Mediator.ai – Using Nash bargaining and LLMs to systematize fairness

Maya and Daniel opened a bakery together two years ago. Now they're stuck.

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, and she can point to four times the hours. Daniel thinks a handshake is a handshake, and he can point to the delivery income that's covered their shared rent for eighteen months, and to the bakery profits he hasn't taken a cut of in over a year. They can't agree. They're about to lose the deal.

Maya and Daniel each walked through their side with Mediator privately. Then Mediator 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.

Or see what each of them wrote privately, before Mediator ran: Maya's statement · Daniel's statement

What Mediator actually did

Maya and Daniel each typed out their side, separately; neither saw what the other wrote. Mediator then drafted a handful of candidate agreements: some closer to Maya's 70/30, some closer to Daniel's 50/50, some nobody had proposed. It scored each one against both accounts and kept the best. Made new drafts from the survivors. Ran it again. Stopped when no new draft could do better.

The scoring comes from a 1950 result by the economist John Nash on fair cooperative bargaining. The math isn't new. What's new is an AI that can read ordinary language well enough to turn a conversation into something that math can operate on.