RAG & Knowledge-base Systems
Retrieval-augmented systems that let your team (or your customers) ask questions in plain English and get answers grounded in your actual documents.
Generic chatbots don't know your product manuals, internal policies, or historical support tickets. RAG systems do. We build retrieval pipelines that chunk, embed, and index your real documents, then wire that retrieval into a chat or search interface with citations, so answers are traceable back to a source instead of hallucinated.
What's included
- Document ingestion pipeline (PDFs, docs, wikis, tickets, whatever you have)
- Chunking/embedding strategy tuned to your content, not a generic default
- Vector search + retrieval-augmented generation with source citations
- Access-control-aware retrieval (users only see what they're allowed to)
- Ongoing re-indexing as your source documents change
How it works
- 01Inventory sourcesIdentify which documents/systems the knowledge base needs to pull from, and who should see what.
- 02Build the pipelineIngestion, chunking, and embedding tuned to your actual content structure.
- 03Wire up retrievalRAG pipeline with citation tracing, integrated into chat, search, or an internal tool.
- 04Keep it currentAutomated re-indexing so the knowledge base doesn't go stale.
Stack
LangChainpgvector / PineconePythonOpenAI/Anthropic embeddings