InsightPilot - RAG Customer Intelligence Agent

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  Real-Time Customer Sentiment & Competitor Review Analytics.

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Inspiration

What it does

How we built it

Challenges we ran into

Accomplishments that we're proud of

What we learned

What's next for InsightPilot - RAG Customer Intelligence Agent

Inspiration

Modern businesses struggle to extract actionable intelligence from customer support logs and review feedback. Standard support bots often lack precise knowledge context, leading to high escalation rates and lost sales opportunities. InsightPilot AI was built to solve this by creating an autonomous, RAG-powered intelligence agent that not only resolves support queries with extreme accuracy but also analyzes feedback and competitor reviews to deliver real-time sales insights.

What it does

InsightPilot AI operates as a unified Customer Intelligence & Analytics Engine:

• Intelligent RAG Support: Resolves complex customer queries with high precision, achieving a 74% Deflection Rate, 1.00 Faithfulness score, and 0.89 Answer Relevancy.
• Sentiment & Feedback Analytics: Processes customer feedback and competitor reviews using NLP sentiment models to uncover key product pain points.
• Sales Intelligence Reports: Automatically converts analytical feedback into actionable sales and marketing insights.
• Customizable White-Labeling: Supports seamless embedding into existing enterprise workflows and brand aesthetics.

How we built it

• Backend & Core AI: Built with Python and FastAPI to handle scalable asynchronous API requests.
• Data & Vector Storage: Leveraged Supabase (PostgreSQL) for vector embeddings and document management in the Retrieval-Augmented Generation (RAG) pipeline.
• Frameworks & Processing: Implemented LangChain for orchestration and Pandas for structured data aggregation and analytics.
• Deployment & Frontend: Hosted the dashboard on Vercel with clean API integration for live metrics visualization.

Challenges we faced

Optimizing vector retrieval latency while maintaining high precision for faithfullness scores was a key challenge. We refined vector chunking strategies and prompt evaluation pipelines to guarantee zero hallucination in real-time resolutions.

What's next for InsightPilot AI

• Expanding multi-channel integration (Slack, WhatsApp, Email).
• Building predictive customer churn models directly into the analytics dashboard.

Built With

• api
• langchain
• powerbi
• python
• rag
• supabase
• vercel