There is no shortage of pressure to do something with AI and very little clarity on what actually pays back. We start with the use case and the number, keep a human in the loop, and leave an audit trail: the model proposes, your rules decide.
Azure OpenAI · Azure ML · Power Automate · Human in the loop · UK-hosted · No training on your data
Every engagement starts by naming the decision being supported and the cost of getting it wrong. If neither is clear, there is no project yet.
We size the opportunity before building anything: what the manual process costs today, what accuracy would be good enough, and what the model has to beat to be worth running.
The model proposes and a person decides, with the reasoning shown. That is what makes the output defensible when somebody asks why a decision was made.
Most of the payback is usually not the model. It is the re-keying, chasing and approvals either side of it, automated in Power Automate and the Power Platform.
Versioned prompts and models, logged inputs and outputs, monitoring for drift, and a clear position on what data goes where — documented for your DPO.
Processed in the UK, not used to train third-party models, with retention and deletion written into the data processing agreement.
Often the problem is a rules engine, a better form or a data fix. We will tell you that before you spend on a model.
One third-sector automation programme, one trained model in commercial use.
Forty-seven automated processes that removed a single point of failure, taking critical back-office work off one person’s desk.
Traders paste a chat message and the enquiry record is created in real time, releasing an estimated 1.5 to 3.3 hours of data entry a day across a 17-trader team.
Microsoft-native AI, so your data stays inside the tenancy and the licensing you already understand.
Public-sector clients get the additional layer the sector expects: alignment to the Algorithmic Transparency Recording Standard where it applies, a DPIA covering the model as well as the data, and a written position on bias, review and escalation.
Same team and same standards either way. The route depends on who signs.
No. Client data is processed in the UK inside your tenancy or ours, is not used to train third-party models, and retention and deletion terms are written into the data processing agreement before anything is connected.
At the decision. The model produces a proposal with its reasoning and confidence, and a person accepts, rejects or amends it. Every step is logged, so the audit trail shows who decided what and on what basis.
In practice: a DPIA that covers the model, a documented position on bias and review, transparency about where the tool is used, and alignment to the Algorithmic Transparency Recording Standard where it applies. We produce those as part of delivery.
We say so, usually during the first fortnight. A rules engine, a fixed data pipeline or a better-designed form solves a good proportion of the problems that arrive labelled as AI, and they cost less to run.
Fixed price for a defined pilot, typically four to six weeks, ending with a measured result against the number we agreed at the start and a go or no-go recommendation.
A short, senior-led conversation — or a free systems and data health check. No hard sell.
