Customer Support RAG Knowledge Base
An AI support agent that answers complex product questions by scraping a company's website and private knowledge base documents.
Client: Confidential B2B SaaS Startup
Summary
We replaced a static FAQ page with a high-performance RAG agent, reducing manual ticket volume by 65%.
What We Built
Delivery Timeline
Day 1-3: The Crawler
Implemented doc-scraping engine.
Day 4-7: Vector Indexing
Established Pinecone index.
Day 8-11: Chat Widget
Developed embeddable chat widget.
Day 12-14: Audit
Completed safety testing.
Architecture
frontend
React with tailwind-css.
backend
FastAPI for concurrent RAG queries.
auth
Secure API keys for cross-origin widget embedding.
data
Pinecone for vectors; PostgreSQL for audit logs.
ai
GPT-4o for natural language understanding.
Security
rbac
Internal/External data filters.
secrets
Encrypted keys for Pinecone and OpenAI.
logging
Full audit trail of every AI conversation.
monitoring
Health check endpoints for crawler and vector index.
Deliverables
FAQ
Frequently Asked Questions
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