RAG development: AI grounded in your own knowledge
Most AI tools answer from what they read on the internet. RAG connects the model to your documents instead — your runbooks, contracts, policies, product manuals and support history — so answers come from your business rather than from a general-purpose average.
We build production RAG systems, not demos: internal knowledge assistants, customer-facing support, and agentic systems that take action. They run on cloud, private cloud or fully local models, depending on how sensitive your data is and where it is allowed to live.
Retrieval-augmented generation (RAG) connects an AI model to your own documents and data, so every answer is grounded in your business — not the internet's average. Read our plain-English RAG guide for the full picture.
What we build with RAG
Internal knowledge assistants that answer staff questions from your own handbooks, runbooks and past tickets, instead of asking the one colleague who remembers everything.
Customer-facing support
Assistants grounded in your published help documentation and product content, answering common questions with citations and handing the rest to your team with the context already gathered.
Agentic RAG that acts, not just answers
Retrieval plus permission to do something about it: open a ticket, restart a service, update a record. We run one in production — watch it resolve a live incident.
Document-heavy workflows
Contracts, compliance evidence and tender responses, where the work is finding the right clause across hundreds of files rather than writing new prose from scratch.
How a RAG project runs
1 · Source audit and data readiness
We look at what you actually have: file formats, duplicates, out-of-date versions, access rules. Retrieval quality is decided here, long before any model is chosen.
2 · Retrieval design and evaluation set
We design chunking, embeddings and search strategy for your material, and write down the real questions the system must answer correctly. That set becomes the score you can hold us to.
3 · Build in two-week cycles
Each cycle ends with working software and a measured answer-quality figure, so progress is visible in results rather than status reports. Priorities can change between cycles.
4 · Handover with monitoring
You get documentation, monitoring of retrieval and answer quality, and the ability to run it yourselves. Ongoing support is available if you would rather we kept an eye on it.
Private by design
Cloud, private cloud or fully local
The same system can run on a public model provider, in your own private cloud, or entirely on local hardware with no data leaving your network.
Scoped away from customer data
Where the use case does not need it, we scope the AI so it never touches customer data at all, and enforce that in the retrieval layer rather than in a prompt.
Built with GDPR and the EU AI Act in mind
We design for data minimisation, logging and human oversight so the system supports your GDPR and EU AI Act obligations — your legal advisers still own the compliance call.
Other Lab0 services
Frequently asked questions
How is RAG different from fine-tuning?
RAG retrieves your current documents at the moment a question is asked, so knowledge is cheap to update and every answer can be traced back to a source. Fine-tuning bakes knowledge into the model's weights, which is slower to change and harder to audit. Most of the systems we build need only RAG, and we will say so if fine-tuning would not earn its cost.
How much does a RAG system cost?
It depends on how many sources you have, how clean they are, and how deeply the system needs to integrate with your existing tools. We cost projects openly: you see where the time goes and where hosting or licensing charges apply. The first conversation is free and includes an honest view of whether the work is worth doing.
Can it run without sending data to US cloud providers?
Yes. We build on local models running on your own hardware, or on EU-hosted providers where you would rather not manage infrastructure. For the strictest environments the whole system can run fully offline, with no outbound calls at all.
How do you stop it making things up?
Answers are grounded in retrieved passages and returned with citations, so anyone can check the source. We build an evaluation set from real questions and measure answer quality against it on every change, with guardrails on what the system is allowed to say and do. When the documents do not support an answer, it says so and escalates to a person rather than guessing.
How long until something is working?
A first working assistant on a real subset of your documents typically arrives within the first two-week cycles. Production hardening takes longer and depends mostly on how many systems it has to integrate with. You see working software at the end of every cycle, not just at the end of the project.
Sound like what you need?
The first conversation is free. You'll get a straight answer on where we can help, and where we can't.