Standard search finds exact keywords. LLM-powered search understands intent — your team asks questions in plain language and gets accurate answers from your internal documentation, product catalogs, or knowledge bases.
We build the full RAG (retrieval-augmented generation) pipeline: document ingestion, chunking, vector embedding, storage, retrieval, and the LLM layer that synthesizes answers with citations.
This is a 2–3 month project because it requires careful data preparation, relevance tuning, and accuracy validation. Rushing it produces hallucinations — we won't cut corners here.
Review your documentation — what exists, what's stale, what's formatted well enough to use.
Build the pipeline that processes, chunks, and embeds your documents.
Configure the vector database and indexing strategy for your data volume.
Connect the retrieval layer to the LLM and tune the prompt for your use case.
Test with real queries from your team. Tune until accuracy meets a defined threshold.
Launch the search interface with monitoring for query quality.
We respond to every enquiry within 24 hours and provide an honest scoping estimate at no charge.
