#building and deal intelligence agent with president memory

A developer built a deal intelligence agent that uses Hindsight AI's episodic memory core to retain objections, competitor mentions, stakeholder concerns and pricing discussions across multi-month sales cycles, replacing stateless RAG retrieval with a persistent deal memory graph. The Python and Streamlit system stores each call or email interaction with metadata and recalls it to generate call briefings and tactic suggestions, addressing the failure of vector databases to link semantically similar objections such as "budget too high" and "pricing concern." The code is published on GitHub.

Sales deals are 3-6 months long with 20+ touchpoints. Reps waste hours re-reading scattered CRM notes before calls and still forget key objections like pricing concerns or competitor mentions. Traditional RAG agents are stateless. They treat every query as new. They give generic info like "Acme is a 200-employee SaaS company" but miss that last week the CTO said "you're 30% more expensive than CompetitorX". Deal Intelligence Agent that remembers every interaction across a deal cycle - objections raised, competitors mentioned, stakeholder concerns, pricing discussions. Over time, it learns which objection-handling approaches work best. We use Hindsight AI as the memory core. Not just vector search - it's episodic memory that stores deal id, objection type, sentiment, and evolves over time. User Call/Email - Transcription - Hindsight.store with metadata - Deal Memory Graph - Hindsight.recall - Briefing + Tactic Suggestion Python, Hindsight AI, OpenAI API, Streamlit for frontend Paste the python code I gave you for store interaction and get briefing Before: "Brief me on Acme" - Generic company info After: "Brief me on Acme" - "CTO pricing objection vs CompetitorX, CFO worried about implementation, you promised ROI calculator, Winning tactic: Comparison sheet + quarterly payment closed 70% similar deals" Challenge: Vector DB couldn't link "budget too high" and "pricing concern" as same objection. Solution: Hindsight's semantic memory. Add your frontend screenshot + briefing output screenshot here Gong integration, Deal health score, Auto email drafting Code: https://github.com/fatimamadiha0333-max/deal-intelligence-agent https://github.com/fatimamadiha0333-max/deal-intelligence-agent Memory: github.com/hindsight-ai/hindsight