Enterprise KnowledgeAI Chatbots2026
Turning a document dump into a conversational knowledge base
A retrieval-augmented platform that lets a team upload or connect proprietary documents and query them conversationally, with answers grounded in the source material.
Challenge
Proprietary knowledge usually lives scattered across documents nobody can search properly — the information exists, but finding it means asking around or digging through folders.
What we built
We built a RAG platform: document ingestion and processing, vector search over the resulting embeddings, and a conversational layer through LangChain that answers from retrieved context instead of guessing.
Results
- —Retrieves and cites source material instead of answering from unconstrained model memory.
- —Handles document ingestion and processing as a pipeline, not a one-off script.
- —Built on vector search and LangChain — the same grounding pattern we'd apply to a client's internal knowledge base.