Generative AI & RAG that answers from your data, not the internet.
Your knowledge lives in a hundred PDFs, a wiki nobody updates and three people's heads. We build assistants and search that answer from your own documents, cite the source, and say "I don't know" when they don't.
The problem you're living with.
Everyone on your team has tried ChatGPT. Some of them are already pasting company documents into it. And the answers sound great until someone checks them.
- New hires ask the same questions every week because the answers are buried in old documents.
- Support agents flip between five tabs to answer one customer question.
- A demo chatbot looked impressive, then confidently made up a policy that doesn't exist.
- Legal and security won't sign off because nobody can explain where the data goes.
Without a real system, you get the worst of both: people quietly using public AI tools with company data, and no trustworthy assistant your customers or staff can actually rely on.
What we build.
Assistants and search that answer from your own documents, and show where the answer came from.
Internal knowledge assistants
Ask a question in plain English, get an answer drawn from your policies, docs and tickets, with links to the exact source.
Customer-facing support assistants
Answer common questions around the clock and hand the conversation to a human, with the full context, when it's out of depth.
Retrieval pipelines (RAG)
Ingest, clean, chunk and index your content, keep it in sync as documents change, and respect who is allowed to see what.
Drafting and summarising tools
First drafts of reports, replies and case summaries, prepared for a person to edit rather than written from scratch.
Guardrails
Topic limits, PII redaction, prompt-injection checks and refusal rules, so the assistant stays inside its job.
Evaluation harness
A set of real questions with known good answers, run on every change, so you can see if quality went up or down before users do.
How it works, step by step.
No big bang. Small, measured steps with a working demo every week.
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Pick the questions
We collect 50 to 100 real questions people ask today. They become the test set everything is measured against.
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Audit the sources
Which documents are current, which contradict each other, who can see what. A RAG system is only as good as what it reads.
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Build the retrieval layer
Ingestion, indexing, access control and search tuned on your test questions, not generic benchmarks.
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Add the model and guardrails
The language model, prompts, citations and refusal rules, evaluated against the test set before anyone sees it.
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Launch small, watch closely
One team or one channel first. We read the logs, fix the gaps, grow the test set, then widen access.
What changes for your team.
What we aim for, measured against how things run today. We don't promise numbers before we've seen your baseline.
Answers in seconds, with sources
People stop hunting through folders and can check the answer in one click.
Onboarding gets shorter
New hires ask the assistant first and experienced colleagues second.
Shadow AI goes away
There's an approved tool that's actually good, so people stop pasting company data into public chatbots.
You can prove quality
Evaluation scores you can show to leadership, legal and auditors, not just a good-feeling demo.
What we'd tell a friend about generative ai & rag
Most failed RAG projects fail on the documents, not the model. Before you pay anyone to build an assistant, find out how many of your "source of truth" documents are out of date. That's usually the real first project.
What we won't do
- Ship an assistant without an evaluation set and call it done.
- Let a customer-facing bot answer medical, legal or financial questions without guardrails and a clear human hand-off.
- Send your data anywhere you haven't approved in writing.
Generative AI & RAG: your questions.
What is RAG, in plain English?
Retrieval-augmented generation means the AI looks up relevant passages in your own documents first, then writes its answer from those passages and cites them. It's the difference between an assistant that knows your business and one that guesses.
How do you stop the AI from making things up?
You can't reduce it to zero, but you can get close and catch the rest. We ground answers in retrieved sources, require citations, tell the model to refuse when the sources don't cover the question, and measure hallucination rate on a test set before and after every change.
Can it respect permissions, so people only see what they're allowed to?
Yes. We carry your existing access rules into the retrieval layer, so a search only returns documents the person asking could already open.
Should we fine-tune a model instead?
Usually not as a first step. Fine-tuning changes how a model writes, not what it knows, and it's harder to keep current. RAG plus good prompts solves most knowledge problems; we'll recommend fine-tuning only when there's a clear reason.
What does it cost to run?
It depends on volume and model choice. We estimate the monthly running cost during discovery, design for caching and smaller models where they're good enough, and set up cost monitoring so there are no surprise bills.
Want this for your team? Let's scope it together.
Tell us how the work runs today. We'll come back within one business day with an honest first take and a suggested first step.
- Weekly demos
- No lock-in
- You own the code
- 1 business day