Ingestion pipelines
Parsing, OCR, and recursive chunking that survive nested tables and long documents.
Retrieval-augmented generation systems that answer from your documents — with citations, not hallucinations — from the data pipeline to the interface.
A base language model is fluent and confidently wrong about your domain. Ask it about your contracts, filings, or product and it invents plausible answers — unusable where accuracy is the point.
Stuffing documents into a prompt does not fix it. Real retrieval is an engineering problem: chunking, embeddings, ranking, and grounding.
We build the full RAG pipeline: parse and chunk your sources, embed and index them, retrieve the most relevant passages, and constrain the model to answer only from what it found — with citations back to the source.
We tune retrieval against real queries and add reflection loops that verify citations before an answer ever reaches a user.
Parsing, OCR, and recursive chunking that survive nested tables and long documents.
Embeddings, hybrid retrieval, and re-ranking tuned to your real questions.
Answers constrained to retrieved context, with citations and self-checks against hallucination.
Conversational and search UIs that make a corpus instantly queryable.
RAG grounds a language model in your own documents. Instead of answering from training data, the system retrieves the most relevant passages from your knowledge base and answers from those — with citations — so responses stay accurate and auditable.
It sharply reduces them. By constraining the model to retrieved context and verifying citations before responding, answers stay anchored to your sources rather than the model's imagination.
Contracts, filings, policies, tickets, wikis, product docs — any corpus where people need fast, cited answers instead of manual search.
With evals on real queries: retrieval relevance, answer faithfulness, and citation accuracy — so improvements are measured, not guessed.