RAG & Knowledge Systems

• Grounded, citation-anchored answers over your content — not a notebook, an operated pipeline
• Defense-in-depth authorization — results pre-filtered and re-checked against permissions before they reach the citation set
• A self-healing index — a daily reconcile re-enqueues missing chunks and removes orphaned vectors
• Your knowledge base exposed to AI agents as MCP tools — the hook into the MCP wedge

How it works

Retrieval-augmented generation lets an LLM answer questions over your content — grounded in your documents, with citations — instead of guessing from its training data. We build it as an operated subsystem, not a notebook demo. You drop your content in; an ingestion worker chunks and embeds it and upserts the vectors idempotently. A query runs through a hybrid retriever — a vector branch and a keyword full-text branch in parallel — and the two result sets are fused with reciprocal-rank fusion, so recall beats vector-alone. An LLM then synthesizes an answer carrying numbered citation markers back to the source, and any marker the model invents out of range is silently dropped.

Authorization is defense-in-depth: candidates are pre-filtered by permission and then re-checked again before they reach the citation set, so a user never sees a citation to content they cannot access. A daily reconcile job keeps the index honest — it re-enqueues chunks that went missing and removes vectors orphaned by deletes.

Built and operated — stated honestly

This pipeline was built and operated in production within the Catalyst lineage. We are framing it exactly that way: built and operated, not a brand-new native product we are inventing for you. The retrieval, embedding, and citation machinery is real, shipped software with the production scar-tissue to prove it ran — and that is the honest claim. We build it for you through the D2D Line and hand it over to own and run.

What you own at the end

The ingestion worker, the hybrid retriever, the synthesis layer, and the reconcile job are yours — source and all. It runs on your infrastructure, and the same retriever can be exposed to your AI agents as MCP tools, so your knowledge system plugs straight into the MCP wedge rather than standing alone.

FAQ

Why hybrid retrieval instead of just vectors? Because keyword and semantic search miss different things. Running both branches and fusing them with reciprocal-rank fusion gets better recall than either alone, and a startup guard catches an embedding-model-versus-collection mismatch before it can silently reject content.

Are answers actually grounded, or can it hallucinate a source? Every claim carries a citation marker back to a real chunk, and markers that point out of range are dropped — so a citation is to something that exists, under the asking user's permissions.

How is it priced? Fixed-scope, fixed-fee. The content sources, the retrieval shape, and the authorization rules are pinned in the FRAME phase before any build starts.

• A queue-driven ingestion worker — embed, persist, idempotent vector upsert — and a hybrid retriever running vector and full-text branches in parallel, fused by reciprocal-rank fusion, with an always-on post-filter re-checking every candidate against permissions; densely test-backed including a real vector-database integration tier.
• LLM-grounded synthesis with numbered citation markers (out-of-range markers silently dropped) plus a daily drift-reconcile that self-heals the index; production fixes confirm it shipped and ran.
• A retrieval-over-MCP tool surface — the retriever exposed as MCP tools under the calling user's identity — bridging this capability to the native MCP wedge.