Problem we're solving
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.
Why normal AI agents forget
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".
Our solution
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.
How persistent memory works
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.
System architecture
User Call/Email -> Transcription -> Hindsight.store() with metadata -> Deal Memory Graph -> Hindsight.recall() -> Briefing + Tactic Suggestion
Technologies used
Python, Hindsight AI, OpenAI API, Streamlit for frontend
How the agent remembers clients/deals
[Paste the python code I gave you for store_interaction and get_briefing]
Example workflow
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"
Challenges & solutions
Challenge: Vector DB couldn't link "budget too high" and "pricing concern" as same objection. Solution: Hindsight's semantic memory.
Demo/screenshots
[Add your frontend screenshot + briefing output screenshot here]
Future improvements
Gong integration, Deal health score, Auto email drafting
GitHub/demo links
Code: https://github.com/fatimamadiha0333-max/deal-intelligence-agent
Memory: github.com/hindsight-ai/hindsight
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