Your team already asks the same questions over and over — buried in docs, wikis, and old tickets. We connect your knowledge sources to a managed retrieval-augmented assistant that answers with citations, and we run the infrastructure so you don't have to hire for it.
Retrieval pipelines are easy to prototype and hard to keep reliable in production.
We host and manage the vector index, embeddings, and retrieval logic — your team never touches server ops.
Every response points back to the source document, so users can verify instead of guessing whether it's accurate.
New and updated documents get re-indexed on a schedule, not manually re-uploaded.
One managed service fee instead of cloud bills that scale unpredictably with usage.
We handle ingestion and tuning, so you're answering real questions in weeks, not after a quarter of internal R&D.
Different teams or customers can be limited to the sources they're allowed to see.
Everything needed to keep a retrieval assistant accurate and running, handled for you.
Connects to your docs, wikis, PDFs, and support history as a unified knowledge base.
We handle embeddings, indexing, and re-indexing as content changes, hosted on our infrastructure.
Responses link back to the exact document and section they were drawn from.
Restrict which sources a given user group or customer segment can query.
Dashboard showing query volume, common questions, and answers with low confidence.
We adjust retrieval quality and prompt behavior based on real usage after launch.
Those are strong developer toolkits, but you still own the infrastructure: hosting, re-indexing, monitoring, and fixing retrieval quality as it drifts. RAG-as-a-service means we run and maintain all of that as a managed product, so your team gets the assistant without owning the pipeline.
We can take over an existing DIY pipeline, migrate it to a managed setup, and stabilize the parts that are usually the hardest to maintain long-term — re-indexing, monitoring, and retrieval tuning.
Yes — the assistant is built to ground every answer in retrieved passages and link back to the source document, rather than relying on the model's general knowledge.
A first version connected to your core documentation typically launches in 3-4 weeks.
Tell us what knowledge sources you want it to answer from — we'll scope a managed setup that stays accurate as your content grows.