AI adoption
Governed AI for organisations that cannot hand over control. Approval gates, bounded actions and auditable records, with the hosting, the model provider and the data flows written down before anything goes live.
What is AI adoption?
AI adoption is the work of putting artificial intelligence to use inside your organisation so that it is both useful and safe: it removes effort, stays under human control, and leaves a record of what it did. For a regulated business, the control and the record matter as much as the time saved.
We build custom software first and treat AI as one governed capability inside it, with security and governance in from the start.
What is included
- An assessment of where AI would help and where it would not, so you adopt it where it earns its place rather than everywhere at once.
- Assistants and applied AI built on a hosting and model provider arrangement agreed with you up front, including UK-hosted deployment where that is what you need.
- Approval gates on anything that matters, a kill switch to stop the system instantly, and an audit trail recording what was proposed, what was approved and what was carried out.
- Governance and adoption guidance, so your people know how to use AI safely and your policies hold up under scrutiny.
The controls we build in
We design AI systems with clear approval gates, bounded actions and auditable records. UK-hosted deployment options are available, and data flows, model providers, storage locations, retention and subprocessors are documented before implementation.
Where the data goes, who approves what, and what is recorded are the things an insurer, an auditor or a cautious board will ask about, so we write them down before the build.
Is private AI safe for a regulated business?
It can be. Public, consumer AI tools are convenient, but you have little say over where your data goes, how it is retained, or whether it trains a model you do not control. For a firm handling sensitive or regulated information, that is often a non starter.
The case for adopting AI: it removes repetitive load, speeds up routine drafting and triage, and frees skilled people for work that needs them. The case against doing it carelessly: a system that acts on its own, leaves no trace and cannot be stopped is a liability.
Our answer is to settle the governance and the data flow before the build: which model provider is involved, where processing and storage happen, what is retained, and which actions need a person to approve them. Where UK-only processing is a requirement, we have built it. The same discipline applies when the assistant is Microsoft's own, which is what our governed Microsoft Copilot adoption is for.
A working example: Ainsley
Ainsley, the assistant in the corner of this page, is one we built. It answers questions about what we do and hands an enquiry to our team when someone wants a person. Behind it sit a kill switch and a logged record of every exchange. It calls a third-party model provider, so it is not our example of UK-only processing: it is our example of a small, bounded assistant with the controls in place. Ask it anything.
Why DSC
The build, the security and the evidence come from one team. The controls go in as the system is built, not at the end to pass a review: gated, bounded, auditable and documented.
Where this connects
Governed AI sits on top of the software it runs in, the integrations that feed it, and the security and governance around both.
Workflow automation
For rules based work, governed automation built with the same controls.
Software and AICustom software
The systems AI plugs into, built around how you work and secure by design.
Cyber SecurityBuilt with security
Applied AI built and run by the same certified team that secures the rest of your estate.
Governance and AuditBuilt with governance
Approval gates and a documented audit trail give you the evidence a regulator or insurer expects.
Common questions
Is private AI safe for a regulated business?
It can be. You know where your data goes, a person approves anything that matters, there is a kill switch, and there is a record of what the system did. Built that way, it is a capability you can defend to an auditor or insurer.
What stops the AI doing something it should not?
Three things, by design. It acts only within boundaries you have set. Anything that carries weight meets an approval gate where a person signs it off first. And a kill switch lets a human stop it instantly. Every step is logged, so if something looks wrong you can see what happened and on whose authority.
Where is the AI and our data hosted?
That is decided with you before the build, not afterwards. UK-hosted deployment options are available, and we have built deployments where inference runs entirely in the United Kingdom. Where a design uses a third-party model provider, we name it, and the data flows, storage locations, retention and subprocessors are documented so you can show an auditor exactly where information goes.
Can we see what the AI actually did?
Yes. What the assistant proposed, what was approved, and what was carried out is written to an audit trail, so there is a clear answer to the question that matters most with any automated system: what exactly did it do, and on whose authority.
Do you only do AI, or software too?
We build custom software as well, and treat AI as one governed capability we apply where it earns its place. You get AI where it helps, built by a team that also runs and secures what it builds.
Talk to us about AI adoption
If there is work AI could take off your team, we will scope it with the controls and the hosting agreed up front.