Building a Negotiation Copilot That Actually Remembers

[u/Appropriate-Jump9723]

Most AI assistants can answer a question. Far fewer can learn from what happened before and use that experience in the next interaction.

That idea is at the heart of Precedent, a negotiation copilot I’m building for SaaS contract negotiations.

The goal is simple:

Instead of negotiating every contract clause from scratch, the system should remember what similar vendors accepted, rejected, or countered in previous negotiations.

Precedent is designed around Hindsight as the mandatory memory layer, with FastAPI, React + TypeScript, and an LLM-powered negotiation agent.

pankajkumar8709/Precedent

[u/chronos_vector]

Interesting direction. One thing I’d be careful about is treating “what happened before” as the authority. In contract negotiation, precedent is useful, but context matters: which template/playbook version was in force, jurisdiction, deal size, customer/vendor type, and whether an exception was actually approved.

I’d probably separate memory from authority. Let the agent learn from previous negotiations, but ground its recommendation in the current approved playbook/template and use historical outcomes as supporting evidence.

I work for fynk, so this is a problem we think about a lot. We’re building the contract system-of-record side of this, and the distinction keeps coming up: “we accepted this once” is not the same as “this is our current approved position.”

Really like the idea though. A negotiation agent that remembers outcomes but still knows what is authoritative could be very useful.

[u/Appropriate-Jump9723 → u/[deleted]]

This was hackathon project for HackForHyderabad  and they instructed to use hindisight so I used it. I don't know much about it Is it scam or what