AI Consulting & Integration
AI that does a job, not a demo.
In your tools
Most AI pilots do not fail because the model is not clever enough. They fail because the AI sits beside the work instead of inside it. Staff have to remember it exists, open a separate tab, paste something in, and copy the answer back out. That is more steps than the task took before, so within a month nobody is using it.
We start from the opposite end: where does your team actually lose time? Re-keying data between systems, summarising the same kind of document, drafting the same kind of reply, hunting for a detail that already exists somewhere. Those are the jobs worth handing to a model, and they are the ones where the return is measurable.
Then we put it where the work happens — inside Microsoft 365, inside Teams, inside the line-of-business system your staff already have open — with clear rules about what data it can see and a human approval step wherever the output reaches a customer. We run this ourselves: our own call platform drafts every client note with Azure OpenAI, and a person approves it before it is filed.
- Hours returned on work your team does every week
- AI inside the tools people already have open, not another tab
- A written policy on what the model can and cannot see
- A human in the loop wherever output reaches a client
- A way to tell whether it is working, in numbers
What the service covers
Readiness assessment
An honest look at where AI would pay for itself in your business and, just as usefully, where it would not. You get a short list of candidate workflows ranked by effort and return, not a strategy deck.
Workflow integration
AI wired into the tools your team already uses rather than bolted on beside them. Microsoft 365, Teams, your ticketing or practice management system, and the line-of-business applications that run your day.
Microsoft 365 Copilot rollout
Licensing, permissions, and the governance work that has to happen first. Copilot surfaces whatever a user can already reach, so we fix oversharing before switching it on, not after.
Custom AI solutions
Purpose-built on Azure OpenAI when off-the-shelf does not fit: document extraction, summarisation, classification, drafting, and retrieval over your own content with citations back to the source.
Data readiness and governance
Deciding what a model may see, where its output is retained, and how that is evidenced. The unglamorous part, and the part that determines whether the project survives review.
Guardrails and human review
Approval steps where output reaches a customer, an audit trail of what was generated and by whom, and measurement so you can tell whether it is actually helping.
Other services
Ready to talk about AI?
A short conversation, an honest assessment, and a clear answer on whether we are the right fit. No pressure and no jargon.