# Building DealPilot: A Persistent Memory Sales Intelligence Agent with Groq & Hindsight > Source: > Published: 2026-09-28 18:03:44+00:00 # Building DealPilot: A Persistent Memory Sales Intelligence Agent with Groq & Hindsight Sales representatives lose hours each week sifting through CRM notes, previous call transcripts, and stakeholder feedback. Traditional AI assistants process each call as a blank slate, leading to repeated mistakes or generic pitches. To solve this, I built **DealPilot** for the HackwithHyderabad Hackathon—a smart sales intelligence agent that uses **Vectorize Hindsight** for long-term persistent memory and **Groq** for lightning-fast inference. ## 💡 The Problem When managing multi-stakeholder enterprise deals, critical context often gets forgotten: - A CFO refuses upfront discounts and prefers contract length negotiations. - An IT Security Lead blocks deals without ISO 27001 audit reports. - A VP of Operations prefers two-slide summaries over long pitch decks. Generic AI bots give blanket advice like "offer a 10% discount to close fast," which can ruin real-world enterprise deals. ## 🛠️ How DealPilot Works DealPilot acts as a persistent memory companion for sales reps: 1. **Cold Start (Interaction 1):** Without prior memory, the agent provides standard, baseline guidance. 2. **Context Memory Retention (Interaction 2):** As call notes and rep feedback are entered, DealPilot ingests them into**Vectorize Hindsight memory banks** . 3. **Tailored Brief Generation (Interaction 3):** When asked for a pre-call brief, DealPilot recalls stakeholder constraints, deal risks, and previous feedback to deliver a hyper-specific action plan. ## 🏗️ Tech Stack - **Groq LLM API:** Powers fast, low-latency reasoning and response generation. - **Vectorize Hindsight Client:** Provides persistent memory recall and context indexing. - **Python:** CLI application structure and JSON deal state management. ## 🔗 Links & Resources