I Gave My Proposal Agent Hindsight of Every Lost Bid
Aura Memory takes an RFP, works out what the client is asking for, writes an 11-section proposal, and then waits. When the deal closes, someone records the outcome: won, lost or pending, the factors that mattered, and a few lines from the debrief. The system turns that into a structured lesson and stores it. The next time a similar RFP arrives, those lessons come back and shape the new draft. The…
Aura Memory is a proposal generation system that analyzes Request for Proposals (RFPs), recalls past outcomes, reflects on them, and then generates a new proposal. The system stores the outcome of each proposal in a structured format for future reference. The memory layer is Hindsight, an open-source agent memory system that stores experiences, retrieves relevant ones, and reasons over them to return answers.
The system uses TanStack Start app, server routes, OpenAI-compatible client, and Redis for its own records. It aims to retain, recall, and reflect on proposal outcomes without the need for a vector store. The real challenge lies in attribution, as the system must accurately link past outcomes to future proposals.
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