RAG Knowledge Assistant
We developed a customer-facing RAG chatbot that uses hybrid search and reranking to provide accurate, cited answers from a knowledge base of 50,000+ product documents. The system handles tier-1 support tickets autonomously with human escalation for complex queries.
Client
Enterprise Client
Industry
AI & Technology
Duration
8 weeks
Tech Stack
Claude API, Vercel AI SDK, pgvector, Next.js, Cohere
What We Built
Key Features
Hybrid Search with pgvector
Combines dense vector similarity and sparse keyword matching for retrieval that handles both semantic and exact queries.
Cohere Reranking for Relevance
Two-stage retrieval with neural reranking to surface the most relevant documents from thousands of candidates.
Cited Answers with Source Links
Every response includes clickable citations linking back to the exact source documents for full transparency.
Human Escalation Workflow
Intelligent routing that detects complex or sensitive queries and seamlessly hands them off to human agents.
Analytics Dashboard
Comprehensive metrics on query volumes, resolution rates, user satisfaction, and knowledge gap identification.
Multi-Language Support
Natural language understanding and response generation in multiple languages for global customer bases.
The Challenge
Problems We Solved
- Ensuring answer accuracy across 50K documents
- Low-latency retrieval at scale
- Handling ambiguous queries gracefully
The Outcome
Results Achieved
- 80% of tier-1 tickets handled autonomously
- 3.2s average response time
- 92% user satisfaction score
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