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:
- Cold Start (Interaction 1): Without prior memory, the agent provides standard, baseline guidance.
- Context Memory Retention (Interaction 2): As call notes and rep feedback are entered, DealPilot ingests them into Vectorize Hindsight memory banks.
- 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
- GitHub Repository: https://github.com/venkatsainuthi47-design/dealpilot-hindsight
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