Retrieval pipelines built on your real data
We build Retrieval-Augmented Generation pipelines that ground LLM outputs in your actual knowledge base, multimodal, hybrid search, re-ranked, and measured for accuracy before they go anywhere near production.
From raw documents to grounded AI answers
Good retrieval is the foundation every reliable AI answer rests on. We get that part right first.
Production RAG pipelines
End-to-end retrieval pipelines built for your data, chunking strategy, embedding model selection, vector store configuration, and retrieval logic tuned to your actual query patterns.
Multimodal RAG (RAG 2.0)
Extending retrieval beyond text to cover diagrams, technical drawings, images, and audio, so your agent can search and reason over your full knowledge base, not just the written parts.
Hybrid search & re-ranking
Combining dense vector search with sparse keyword retrieval and adding re-ranking layers that surface the most relevant context before it reaches the LLM.
RAG evaluation & quality
Systematic measurement of retrieval accuracy, answer faithfulness, and context relevance, with the tooling to catch regressions before they reach your users.
The LLM is only as good as what you give it
Your data is your moat
A generic LLM knows the world. A RAG system knows your business, your documents, your domain conventions, your institutional knowledge. That specificity is what makes the output trustworthy.
RAG 2.0 is multimodal
The 2026 standard is retrieval that spans text, images, technical drawings, and audio. If your RAG pipeline only reads text, it is already leaving a large fraction of your knowledge base unreachable.
Retrieval quality determines answer quality
The best LLM in the world gives wrong answers when it is handed the wrong context. Getting retrieval right is where production RAG systems succeed or fail.
Want an AI that actually knows your business?
Tell us about your data and what you need the AI to answer. We will design the retrieval architecture that makes it accurate.
