Ingestion & chunking
We pull in your PDFs, docs, tickets and databases and chunk them on structure — never mid-table — so retrieval is accurate from the first query.
AI · RAG
RAG development — an AI assistant that answers from your data
We build retrieval-augmented AI assistants grounded in your own documents, contracts, tickets or codebase — answers with citations, not confident guesses. The kind we shipped in our own product, Upwora.
4–8 wks
to a production assistant
Cited
answers, or it refuses
Your data
never leaves your boundary
Most RAG demos work because the dataset is twelve documents.
Real corpora are messy, contradictory and large — and that is exactly where naive RAG falls over. The fix is rarely a bigger model; it is retrieval done properly. That is the part we obsess over.
What we build
The parts that decide whether it works.
Retrieval & reranking
Vector search gets you close; a reranking step gets you correct. This one layer kills most hallucinations before they reach the user.
Grounding & citations
Every answer cites its source, and a confidence gate makes the assistant say "I don't know" instead of inventing — the difference between a toy and a tool you trust.
Production interface
A clean chat surface embedded in your app, site or an extension, with logging and evals so you can see and improve what it does.
How we work
Scoped, shipped weekly, handed over clean.
01
Scope your data
We map your sources, the questions buyers or staff actually ask, and what "good" looks like — with a fixed estimate before any code.
02
Build & measure
We ship a working assistant in a live environment weekly, measuring hallucination rate and retrieval accuracy so quality is a number.
03
Harden & hand over
Guardrails, evals, monitoring and readable code your team can own — no lock-in, and support that stays after launch.
Where it fits
What teams hire us to build.
- Internal knowledge assistant over company docs
- Customer support grounded in your help centre
- Contract and policy Q&A with citations
- Codebase and API documentation assistant
- Research and analysis over private datasets
- Sales enablement over product and pricing docs
Questions
What clients ask before we start.
What does RAG development actually involve?
Retrieval-augmented generation grounds an LLM in your own data. We build the ingestion pipeline, chunk and embed your documents, add retrieval and reranking, and wire it into an interface — so answers cite your sources instead of hallucinating.
How is this different from just using ChatGPT?
ChatGPT answers from its training data. A RAG assistant answers from your documents, contracts, tickets or codebase, with citations — and says "I don't know" when the answer isn't there. That's the difference between a demo and something you can put in front of a customer.
How long does a RAG assistant take to build?
A focused production assistant on your data is typically 4–8 weeks, depending on how messy the source data is and how many integrations it needs. You get working software in a live environment each week, not a status deck.
What stops it from hallucinating?
Three things we build in: chunking on document structure so retrieval is accurate, a reranking step that fixes most bad matches, and a confidence gate that refuses to answer on low-relevance queries. We measure hallucination rate before and after so it is a number, not a promise.
Can it run on private or on-prem data?
Yes. We build with your privacy constraints up front — self-hosted models or a vetted API, your own vector store, and no data leaving your boundary if that is a requirement.
Related services