Private & Custom AI Models
Private AI models, trained for your business.
A private AI model, built and trained on your own business data and run inside your own systems, so it understands how your business actually works, and none of your data ever leaves.
Why this exists
Off-the-shelf AI knows the internet.
It does not know your business.
A generic model gives generic answers. A private model learns your documents, your processes and your language, and keeps every bit of it yours, inside your own environment.
What we build
A model that is yours, end to end.
A private AI model
Your own AI model (the same kind of technology behind ChatGPT), tuned to your industry, your terms and your tone, so answers sound like your business, not the generic internet.
Trained on your data
Your documents, tickets, contracts and know-how become the model’s expertise, through fine-tuning and retrieval (RAG).
Deployed privately
It runs in your own cloud or on your own servers, never a shared public tool. Your data and your questions stay inside your business, under UK data rules.
Agents that do the work
Custom AI assistants, chatbots, an AI receptionist, back-office helpers, that don't just answer, they take action across your own systems.
The whole point of private
0%
Your data, owned by you
0
Shared to train public models
UK
Data residency & hosting
24/7
Runs in your environment
What a build includes
From your data to a working model.
One senior team, one clear scope. We build it, prove it, and hand you something that runs.
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Data & use-case discovery
we map what you have and what the model needs to do.
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Model selection
open models (Llama, Mistral) or hosted, matched to your needs.
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Fine-tuning on your data
so the model speaks your domain, not the generic internet.
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Answers from your own documents
retrieval (RAG) so every answer is pulled from your real, current files, with sources, not guesswork.
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Private deployment
in your own cloud or on your own servers, your data never leaves.
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Guardrails & evaluation
accuracy testing and safety limits before anyone relies on it.
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Integration & agents
wired into your tools, with agents that do real work.
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Knowledge transfer
your team can run and retrain it, you are not locked in.
How a build runs
We start with your data, then we ship.
- Week 1
We map your data sources, the use cases that matter, and where the model has to run.
- Weeks 2–4
We pick the model, prepare your data, and stand up a first private prototype to test.
- Month 2 onward
We fine-tune, add retrieval and guardrails, deploy privately, and integrate it into your work.
Scoped to you
Priced to your data and deployment.
- Fixed-scope build
- Optional managed run
- Your environment, your data
Who it’s for
Sound familiar?
Our sweet spot: UK-based firms with knowledge or data too valuable, or too sensitive, for public AI.
- You hold sensitive or regulated data that cannot go to public AI tools.
- You have years of internal knowledge you want to turn into a capability.
- You want answers in your own context, not generic ones from a public chatbot.
- You need AI that runs under your control and under UK data rules.
Where it fits
Three ways to work with us.
Built on open and frontier models alike
- Llama
- Mistral
- Anthropic
- OpenAI
- AWS
FAQ
Good questions.
Does my data leave my environment?
No. The model is deployed in your own cloud or on your own servers, and your data and prompts stay inside it. Nothing is sent off to train someone else’s model.
Which models do you use?
Open models such as Llama and Mistral when privacy or cost matter, or a hosted model where that fits better. We match the model to your use case rather than forcing one stack.
What does “trained on our data” actually mean?
A mix of fine-tuning and retrieval (RAG), so the model knows your documents, terminology and processes and answers in your context, not the generic internet.
Is this GDPR-friendly?
Yes. UK data residency and a model that does not share your data with third parties are core to how we build. Your knowledge stays your asset.
Let’s talk
Thirty minutes. No slides.
We’ll map what a private model could learn from your business, and where it should run.