#building and deal intelligence agent with president memory
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…
Sales deals often span 3 to 6 months and involve over 20 touchpoints. Sales representatives spend hours re-reading disorganized CRM notes before meetings, yet still forget crucial information, such as pricing concerns or competitor mentions. Traditional AI agents using Retrieval-Augmented Generation (RAG) are stateless, meaning they treat each query as new and provide generic information without recalling specifics from previous interactions.
The proposed solution is the Deal Intelligence Agent, which maintains persistent memory throughout a deal cycle. This agent remembers every interaction, including objections raised, competitors mentioned, stakeholder concerns, and pricing discussions. Over time, it learns which objection-handling approaches are most effective. The core of this system is Hindsight AI, which serves as the memory core.
Instead of relying solely on vector search, Hindsight AI employs episodic memory to store deal_id, objection type, sentiment, and evolves over time.
The system architecture comprises several components: User Call/Email, which transcribes conversations into text; Hindsight.store(), which records the interaction along with relevant metadata in the memory; Deal Memory Graph, which organizes and links related memories; and Hindsight.recall(), which retrieves pertinent information when needed. The agent then generates a briefing along with tactical suggestions.
Technologies utilized in this project include Python, Hindsight AI, the OpenAI API, and Streamlit for the frontend interface. To illustrate how the agent remembers clients and deals, the exact Python code for store_interaction and get_briefing functions is provided.
For example, before using the agent, a sales representative might briefly be briefed on Acme, receiving generic company information. After engaging with the Deal Intelligence Agent, the briefing would include details about a CTO pricing objection directly related to a competitor, CFO concerns about implementation, a promised ROI calculator, and a winning tactic that involved presenting a comparison sheet and offering a quarterly payment plan. This approach has been particularly effective, closing 70% of similar deals.
Challenges encountered during the development included the inability of traditional vector databases to link related objections, such as "budget too high" and "pricing concern," as the same issue. To address this, the solution leverages Hindsight's semantic memory, which provides a more nuanced understanding of related concepts.
A demo showcasing the frontend interface is included, displaying the briefing output generated by the agent. Future improvements may involve integrating with Gong for enhanced call recordings, implementing a deal health score system, and automating email drafting based on the agent's suggestions. The complete code for the Deal Intelligence Agent is available on GitHub at https://github.com/fatimamadiha0333-max/deal-intelligence-agent, and the memory system is hosted at github.com/hindsight-ai/hindsight.
Written by urgent.news from Dev.to's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
