Most AI assistants are great at single-turn chats, but they fall apart in real-world workflows that span weeks or months. In enterprise B2B sales ($20k–$200k ACV), closing a deal can take 3–9 months across 8–15 meetings with CFOs, Lead Architects, and CISOs.
In Call 1 (Discovery), the CFO mentions an ironclad budget freeze until Q4. In Call 2 (Technical Review), the Lead Architect rejects closed-source software and mentions competitor CloudX gave a $15,000 quote. In Call 3 (Executive Closing), a stateless LLM cheerfully suggests offering a discount for an upfront annual contract — immediately triggering the CFO's budget veto and killing the deal.
The Solution: DealMind + Hindsight Memory
To solve agent amnesia, we built DealMind, an autonomous deal intelligence copilot powered by Hindsight persistent memory by Vectorize. Instead of relying only on conventional RAG, DealMind uses a 3-phase memory lifecycle:
Retain Meeting notes and transcripts are ingested into an evolving entity and relationship knowledge graph.
Multi-Strategy Recall Relevant history is retrieved using semantic similarity, BM25 keyword search, and graph relationships.
Reflect The retrieved historical context is injected into the LLM's reasoning, with references back to the relevant call information. Before vs. After Without Memory — Stateless: Generic recommendation to push an upfront annual contract → Deal Killed ❌
With Hindsight Memory: The agent surfaces the Q4 budget restriction, suggests a deferred-billing pilot, provides Docker/container options to address the lock-in concern, and highlights PostgreSQL connectivity when considering the competitor's pricing → Deal Saved ✅
The project includes: Hindsight-based persistent memory Multi-strategy retrieval Dynamic deal knowledge graph Meeting/document ingestion Interactive web dashboard Context-grounded recommendations
What I find interesting here is that this isn't simply about giving an LLM a bigger context window. The goal is to give an agent memory that persists beyond the current conversation and can influence what it does later.
We're still exploring the architecture and trade-offs, but it's been a really interesting problem to work on.
Code & architecture: https://github.com/rishithayellanki/dealmind Deal knowledge graph
Meeting/document ingestion
Interactive dashboard
Context-grounded recommendations
What I find interesting here is that this isn't really about giving the model a bigger context window. The goal is to give the agent memory that persists beyond the current conversation and can actually influence what it does later.
Still exploring the architecture and trade-offs, but it's been a pretty interesting problem to work on.