RAG & Knowledge Systems
Ask questions of your own documents, and get citations back.
Retrieval-augmented systems over your private content — contracts, manuals, policies, research, support history — that answer in natural language and cite the source span they used. Built with an evaluation set from day one, because a RAG system nobody measures is a RAG system nobody should trust.
Typical timeline: 4–10 weeks depending on document volume and how clean it is
When this is the right call
Problems this solves
In the words people actually use when they get in touch, rather than in the words the technology uses.
- “The answer exists in a 400-page manual and nobody can find it”
- “New staff take months to learn where things are written down”
- “Search returns documents; people need answers”
- “A vendor built you a demo that worked on five questions and fell over on the sixth”
Deliverables
What you get
- An ingestion pipeline over your documents, with incremental refresh
- A retrieval index tuned against your actual questions
- A question interface — web, chat, or an API into your own product
- Citations that point at the source span, not just the document
- An evaluation set and metrics, so changes can be proven rather than felt
- Architecture documentation and a cost model
Approach
How it is built
- Hybrid retrieval — vector plus keyword — because pure vector search misses exact terms
- Reranking, which usually improves answers more than a bigger embedding model
- Retrieval and answer quality measured separately, since they fail for different reasons
- Groundedness checks: an answer that cannot be traced to a retrieved span is flagged, not shipped
Process
How it runs
- 1
Document audit
Formats, volume, quality, how often it changes, what contradicts what.
- 2
Golden set
Real questions with known good answers. Built before the system, not after.
- 3
Retrieval build
Chunking, embedding, hybrid search, reranking — tuned against the golden set.
- 4
Generation and citations
Grounded answers, refusal on weak retrieval, source spans linked.
- 5
Evaluate, then deploy
Numbers before launch, regression suite after.
What it costs
Scope and price agreed on a short call — no obligation, and you get a written figure before anything starts.
Get a figureWhat I need from you
- The documents, or access to where they live
- Twenty to fifty real questions with answers you would accept
- A subject expert for a few hours to settle disagreements between sources
- A decision on who may see what, if access control is needed
Not included
- Fixing contradictory source documents — I will identify them; resolving them is yours
- Fine-tuning a foundation model (rarely the right answer for this problem, and I will explain why)
- Model API and vector storage costs
- Guaranteed accuracy on questions your documents do not answer
Related work
Something similar, already built
Motorcycle Match
A recommender that tells you why
A working recommender where each suggestion states the constraints it satisfied and the attributes it matched on.
Read the case study →NKable.com — this site
A build pipeline that refuses to publish a dishonest sentence
231 tools live across ~2,900 prerendered pages in nine languages, an agent-native surface (MCP endpoint, llms.txt, RFC 9421 signatures, .well-known manifests), a daily retention sweep, and full sitemap coverage. The audit and every fix are documented in the repo.
Read the case study →RAG & Knowledge Systems — questions people ask
How is this different from a chatbot?
A chatbot is a delivery surface; RAG is what makes its answers come from your content. Many chatbot projects are RAG projects that were scoped as chatbots and then underspecified.
How do we know the answers are right?
A golden set of real questions with known good answers is built before the system is, and retrieval and answer quality are scored against it. You get the numbers, and a regression suite that reruns them on every change.
Can it stay current as documents change?
Yes — incremental refresh is part of the ingestion pipeline. Stale answers are the most common way these systems quietly rot, so refresh is designed in rather than added later.
Does our data leave our control?
That depends on choices we make together: which model provider, hosted or self-hosted embeddings, where the index lives. I will lay out the options with their real trade-offs before we build.
What if retrieval finds nothing relevant?
The system says so. An honest "this is not covered in your documents" is a correct answer, and it is far more valuable than a fluent guess.
Can this sit inside our own product?
Yes — delivered as an API you call, with the same evaluation and citation guarantees.
Start here
Tell me what you are trying to do
You get a written figure and a scope before anything begins. If this is not the right service for the problem, I will say which one is — or that none of them are.
+91 99991 03353
Opens the chat with your message already written. Usually answered the same day.
[email protected]
Opens your mail app with the subject filled in. Better for anything long.
Roughly what you have already, what a good outcome looks like, and any budget or deadline you are working to — that is enough for a first reply with a real figure in it.