Ground your models in your own data — fewer hallucinations, always-current answers.
A query is embedded, the right context retrieved from your knowledge base, and the model answers from it. Tap a stage.
Retrieval-Augmented Generation lets your LLM reference your authoritative internal knowledge — docs, tickets, contracts, codebases — at query time, instead of relying purely on training data. We design the full pipeline: ingestion, chunking, embedding, vector storage, retrieval, and re-ranking.
We tune retrieval quality iteratively against real queries from your team, and build freshness pipelines so your knowledge base stays current as source documents change.
Automated processing of documents, wikis, code, and structured data into retrievable chunks.
The right embedding model and vector store for your scale, latency, and cost.
Hybrid search and re-ranking that surface the genuinely relevant context.
Sync that keeps your knowledge base current as sources change.
Every stage, tuned against your real queries — not a generic template.
Turning documents, wikis, code, and data into clean, retrievable chunks.
Choosing and operating the embedding model and vector DB for your needs.
Finding the right context and re-ranking the best of it for the model.
Combining keyword and vector search for recall and precision.
Measuring retrieval quality against real queries and improving it iteratively.
Keeping answers current as your source documents evolve.
RAG is usually the fastest, cheapest path to trustworthy AI on your data.
Answers stay tied to retrieved, authoritative context instead of guesses.
Update the source, not the model — no retraining required.
Responses can point back to the document, building user trust.
Retrieval at query time, with access controls — no need to train on it.
Grounding is often far less costly than custom training, with faster iteration.
One governed knowledge layer can power many assistants and workflows.
Retrieval quality is an engineering discipline — we treat it like one.
The right vector store, embeddings, and evaluation for your data and scale.
Connect your documents, wikis, code, and data sources.
Split content sensibly and embed it with the right model.
Store vectors in a database tuned for your scale and latency.
Find and re-rank the most relevant context per query.
Measure retrieval quality on real queries and improve it.
Consultation is free. Point us at your knowledge — we'll make it answerable.
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