Overview
Built an enterprise conversational AI platform that enables businesses to deploy custom chatbots powered by their own knowledge bases. The system uses Retrieval-Augmented Generation (RAG) to ground LLM responses in company-specific data, dramatically reducing hallucinations.
Key Features
- Multi-tenant architecture supporting 50+ concurrent organisations
- RAG pipeline with vector search (Pinecone) for document retrieval
- Real-time analytics dashboard showing conversation metrics, sentiment analysis, and user satisfaction scores
- Custom fine-tuning interface allowing non-technical users to improve bot behaviour
- Webhook integrations with Slack, Teams, and Zendesk
Technical Highlights
The backend runs on Node.js with a FastAPI microservice handling ML inference. We implemented a custom chunking strategy that improved retrieval accuracy by 34% over naive splitting. The frontend is a Next.js app with real-time WebSocket updates for live conversation monitoring.
Results
- 40% reduction in support ticket volume for pilot clients
- Average response accuracy of 92% on domain-specific queries
- Sub-2-second response latency at p95
