RAG-Powered Knowledge Base
Natural language search across internal company documentation
A mid-size company with 200+ employees had documentation scattered across Confluence, Google Drive, Notion, and internal wikis.
The Challenge
A mid-size company with 200+ employees had documentation scattered across Confluence, Google Drive, Notion, and internal wikis. Employees spent an average of 25 minutes per search session hunting for information, often giving up and asking colleagues instead. They needed a unified search system that could understand natural language questions and return accurate answers from any source.
Our Solution
We built a retrieval-augmented generation system using LangChain for document ingestion and chunking, Pinecone for vector storage and similarity search, and the Claude API for answer generation. Connectors pull documents from Confluence, Google Drive, and Notion on a scheduled basis, automatically re-indexing when content changes. The React/Next.js frontend provides a conversational search interface where employees type questions in plain English and receive cited answers with links to source documents.
The Results
Reduced average search-to-answer time from 25 minutes to under 4 minutes — an 85% improvement
Adoption reached 78% of employees within the first month of launch
Reduced internal Slack questions by 45% as employees found answers independently
Technical Approach
LangChain orchestrates the document processing pipeline — chunking, embedding generation, and retrieval chain construction. Pinecone was chosen for vector storage because of its managed infrastructure and low-latency similarity search at scale. The Claude API generates answers from retrieved context with citation tracking, ensuring every answer links back to its source document. Next.js provides server-side rendering for the search interface, ensuring fast initial loads.
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