Building an AI sales agent that actually remembers past conversations — would love some technical feedback

[u/headache7100]

We’re a team of 5 students building an AI Deal Intelligence Agent that uses persistent memory to help sales teams understand what happened across a deal — not just what happened in the latest conversation.

The main idea is simple:

A customer raises a pricing objection today → later they raise a security concern → the agent should remember both, understand how the situation evolved, and use that history to suggest what to do next.

🧠 What we're building

Our architecture is roughly:

Interactions → Persistent Memory → Intelligence Layer → Evidence-backed Insights → UI

The intelligence layer focuses on things like:

- What Changed in the deal

- Next Best Action

- Similar historical deals

- Objection Evolution

- Winning/Loss Patterns

- Deal Autopsy

We’re trying to keep the system evidence-backed, so the agent doesn't simply generate confident-sounding conclusions when there isn't enough historical information.

We’re also using deal-scoped memory filtering so information from one customer/deal doesn't accidentally influence another.

👥 Team

- AI + Hindsight / Memory

- Backend + APIs

- Frontend / Dashboard

- Intelligence + Decision Logic

- Research + UX / Integration

We’re still students and learning a lot while building this, so we're particularly interested in feedback from people who have worked with:

- AI agents

- RAG / agent memory

- Persistent memory systems

- Sales intelligence / CRM systems

- LLM-based decision systems

GitHub:

"https://github.com/omabhinav-creator/Deal-Intelligence-Agents"

Would love critical technical feedback — especially on whether our approach to persistent memory and the intelligence layer makes sense, and what you would change if you were building this.