AI Studio
AI that plugs into the systems you already run.
Agents, retrieval and automation built against your own data, with permissions and an audit trail rather than a demo. We start from a job you can name, not from a model.
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Ways we can help
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Fixed
Scope agreed before work starts
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Day one
An engineer on the first call
How we approach it
A pilot you can judge, before a programme you cannot.
One job at a time
A named task with a written success test. Anything vaguer cannot be evaluated, and will not be trusted with a write.
Your data, your boundary
We work out early what may leave your estate and what may not, and build to that rather than around it.
A person on the irreversible
Reads can be automatic. Anything that spends money, sends a message or cannot be undone waits for approval.
A record you can read
Every run leaves a log a non-technical colleague can follow. Without it, nobody can say what happened.
Build with AI
Put it to work on something specific.
Systems that carry out a job you can name, against your own data, with a record of what they did.
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Custom AI agent development
Agents that complete work, not describe it
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Custom MCP server development
Let an agent operate your systems safely
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AI voice agent development
Calls answered, triaged and logged
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AI workflow automation
Joins the tools you already pay for
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Use what you already have
Make the information you hold answer questions.
Most organisations already own the answers. They are spread across a CMS, a shared drive and eleven years of PDFs.
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Search and knowledge assistants
Answers from your content, with sources
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AI chatbot integration
An assistant that knows your business
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AI document processing
Invoices, forms and contracts into data
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AI-powered personalisation
Different content for different visitors
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AI search readiness
Be the source an AI answer cites
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Plan, run and protect
Decide what is worth doing, then keep it safe.
The part that decides whether an AI project survives contact with a real business, an auditor and a budget.
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Enterprise AI consulting
Discovery and roadmap, then the build
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AI readiness audit
What to automate, and what to leave
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AI monitoring and observability
What it did, what it cost, what broke
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AI security and governance
What it can reach, and proving it
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Private and self-hosted AI
Models on infrastructure you control
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Alongside the rest
AI work rarely arrives on its own.
An assistant needs somewhere to run, a system to read from and someone to keep it patched. The rest of what we do is usually part of the same project.
Not sure which of these you need?
Tell us what is taking your team the most time. We will say which of this is worth doing, and which of it is not.