RAG Knowledge Systems
The retrieval infrastructure underneath every grounded AI feature — embeddings, indexing, and citation.
Retrieval-augmented generation is the architecture underneath most of the AI features we build — it's what lets an AI system answer from your actual content instead of general model knowledge. As its own solution, it's the infrastructure layer: document ingestion, chunking, embeddings, vector storage, and retrieval logic tuned to your content's structure.
We build this as reusable infrastructure where it makes sense — a knowledge base that powers both a customer-facing assistant and an internal one, for example — rather than a one-off pipeline duplicated for every feature.
Representative outcomes
Reusable retrieval infrastructure
One knowledge base can power multiple AI features instead of duplicated pipelines.
Grounded, citable answers
Every AI response traces back to a specific source document.
Content stays current
Ingestion pipelines keep the knowledge base in sync as source content changes.
Delivered through
Related work
Relevant industries
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