Agentic AI

Most AI projects fail on plumbing, not models

The model is the easy part. What decides whether an AI rollout works is unglamorous: is your data where you think it is, do permissions reflect who should actually see what, and is there an owner for the output when it is wrong.

We approach agentic AI the way we approach any infrastructure — assess, design, deploy, then support. It starts with a readiness assessment across your tenant, your data estate and your permissions model, and we will tell you plainly if you are not ready yet.

Fix oversharing before you switch Copilot on

Microsoft 365 Copilot answers using everything the user can already reach. In many tenants that quietly includes a SharePoint site somebody opened to the whole organisation years ago. We audit and remediate oversharing, apply sensitivity labels and data-loss-prevention policies, and restrict what agents may ground on — so Copilot becomes useful without becoming an incident.

Agents that do a specific job

Beyond chat, we build agents scoped to real tasks: triaging inbound requests, extracting data from documents, answering staff questions from your own policies, drafting routine replies for a person to approve. Built with Copilot Studio or Azure AI services, connected to systems you already run.

Every agent gets defined boundaries, a human approval step where the stakes warrant one, and logging so you can see what it did and why.

Adoption is part of the project

A licence nobody uses is pure cost. We run practical enablement for the teams who stand to benefit, measure what actually gets used, and move licences away from where they sit idle.