Service lane
RAG & knowledge systems
Your knowledge is scattered across documents, wikis, tickets, chat history, and databases. RAG turns that pile into something your tools and agents can actually query: the right passage retrieved the moment it's needed, with a citation back to where it came from.
How it works
01
We structure your knowledge
Documents, notes, and databases land in one clean index.
02
A question finds its context
Retrieval picks the right passages from vector memory.
03
An answer with its source
The model answers from your data, with the source attached.
01
What it is
RAG (retrieval-augmented generation) means a model answers from your knowledge instead of only what it was trained on. Your content is ingested, split into passages, and embedded into vector memory. When a question comes in, the closest passages are retrieved and handed to the model as grounding. The answer is built from your material and can point back to it. That's the difference between a confident guess and a sourced answer.
02
What it's good for
A few shapes this takes in practice:
- Ask your own documentation in plain language and get an answer grounded in the actual page, with a link to it.
- An agent checks your knowledge base before it acts, so its decisions rest on what's true in your data right now.
- Support answers pull from the same source of truth every time. No two people giving contradictory versions.
- New material is added and it's queryable within the flow. The knowledge stays current instead of frozen at build time.
03
What's possible
Ingestion from the sources you already keep: documents, wikis, databases, tickets, transcripts. Chunking and embedding tuned to your content. Vector memory for semantic search, on its own or alongside keyword and metadata filters. Reranking so the best passage wins, not just the nearest. Citations back to the source, access controls so retrieval respects who may see what, and refresh so new material flows in without a rebuild.
04
What you gain
- Answers you can trust, grounded in your material, with a citation you can open and check.
- Less hallucination: the model reasons over retrieved facts instead of filling gaps from thin air.
- Knowledge that stays put. Expertise lives in a system, not only in the heads of the people who happen to know.
- Current by default: update the source and the answers move with it, no retraining.
05
Who it's for
Teams sitting on knowledge that's hard to search: long docs, sprawling wikis, years of tickets and threads. Anyone whose people keep answering the same questions by hand. Every agent or assistant that needs to be right about your specifics, not just fluent in general. If your data changes and the answers have to change with it, this is the lane that keeps them honest.
06
How we build it
We start from your sources and from how people actually ask. Then we build the pipeline to match: ingestion and chunking shaped to your content, embeddings and a vector store, retrieval tuned with reranking and the right filters, citations wired through so every answer traces back. Access controls come in at retrieval, not as an afterthought. Refresh keeps it current. The same people build it and run it, so as your knowledge grows the system keeps finding the right passage.
07
Examples
Illustrative, not case studies. Every setup is scoped to your own sources and access rules:
- An internal assistant that answers staff questions from your handbooks and wikis, citing the exact section each time.
- A support layer that drafts replies grounded in your product docs and past resolved tickets, so answers stay consistent.
- A retrieval layer that feeds your agents the right context before they act: the same knowledge base, queried by software instead of people.
Want this wired into your stack?
OTW builds and runs it end to end. Join the waitlist for early access, or write to us and we'll work out what to wire first.