We build AI systems, then we run them.
AI ambition has outrun governance at most mid-sized firms, and the Privacy Act's automated decision-making provisions start applying on 10 December 2026. We build the agents that do the work, and the controls that let you defend them.
What we build
Every agent comes down to two choices: does it answer, or does it act, and where does it run. Settle those and the budget conversation gets short. Start with the first option unless you know you need the third.
Knowledge agents on Copilot
Grounded in your corporate knowledge, inside your Microsoft tenant, subject to the access rules your staff already follow. Nothing to provision, nothing leaving an environment you govern today. Where most firms should start.
Locally hosted agents
The same job on open-weight models, on infrastructure you control. Built for firms whose data cannot reach a third-party API, or where hosting cost matters at volume. Sometimes the licence is the cheaper answer, and we will say so.
Process agents
These take action: routing a mailbox, working an outreach sequence, applying a rule the business already relies on. They need the narrowest permissions that let the job finish, plus a review step. We keep the decision in code, where an auditor can read it.
Results
A national accounting partnership had clients it had not spoken to in ninety days or more. We put a Sales Development Rep agent on it. It reached out and warmed up those contacts with the kind of conversation that normally takes a partner: relevant, pitched at the right level, and paced by email so it did not push. It kept each thread moving forward, slowly, and developed and qualified the up-sell and cross-sell opportunities it found. Only when a contact crossed the handover threshold did it bring a real partner into the conversation. A second agent checked every draft against the firm's language rules before it left.
How we work
Readiness first, use cases second, value third, governance fourth.
Every engagement opens on readiness and shadow AI risk before any use case is named. Most firms do not know what their systems can already reach, or which AI their people are silently using, and that exposure sets the boundary for everything built after it. So we look at it first.
Most AI programmes fail on sequencing. Governance written before anyone has agreed what to build becomes a policy nobody implements. A long list of unqualified use cases gives the board nothing to fund.
So we find the use cases with you, rank them against what the business gains, and mark the ones to defer. Governance attaches to real decisions, and to the systems those decisions touch. That is the point at which controls are worth writing.
The readiness assessment
A fixed-fee readiness assessment. We map what your tenant and shared drives already expose, where data leaves the organisation, and which AI services your people may already be sharing your company information with. You get a remediation sequence ordered by risk, and you keep it whether or not you engage us.
Most mid-sized organisations already have AI in use that never passed through IT: staff pasting data into consumer tools, a team trialling a coding assistant, a chatbot built on someone's laptop. None of it shows up in procurement. It is called shadow AI, and it is usually where the exposure is.
You can see it for yourself before we talk. Run the free AI Exposure Check from within your network. It scans several hundred known AI endpoints from the machine you run it on, and reports what is reachable, what your controls are already stopping, and how your traffic is being inspected. A few minutes, in the browser, no cost, no email required. Export the result and bring it to the discussion. Run it again on a schedule and it becomes part of your governance programme: each scan is compared against the last, so newly reachable services are flagged the moment they appear, and your shadow AI risk state is confirmed as things change.