Service
Working AI is not adopted AI. We design the controls, the enablement and the measurement that turn a deployed capability into a used one.
Who it is for
Problems we solve
How we help
Typical deliverables
An adoption assessment and roadmap, then enablement and pilot-to-production support alongside your teams.
Technology fit
We are independent: no reseller agreements and no partner quotas. If your estate points a different way, we will say so.
Governance & production readiness
Adoption stalls on unanswered governance questions far more often than on technical ones. Writing the control model down is usually the fastest way to unblock a rollout.
Example outcomes
Anonymised and sector-level. Client names are withheld by design — the constraint and the architecture are the parts that carry any information.
Multi-sector
The constraint. Delivery teams shipping inconsistently, with standards that existed as documents nobody followed.
Reusable CI/CD workflows, Terraform modules, pre-commit checks and semantic versioning, delivered alongside hands-on mentoring — so the practice stayed with the team after we left.
Life sciences
The constraint. Retrieval over sensitive research material, where the model must never surface content the user is not entitled to see.
The control model was designed before the model was chosen: entitlement-aware retrieval, IAM boundaries and audit logging built in from the first commit. The result was a proof of concept that could credibly become production, rather than a demonstration that quietly stalled at the security review.
Questions
Usually because the tool was deployed without changing how the work is done. People carry on as before and paste the result in afterwards. The second most common cause is an unanswered governance question that nobody wrote down, which quietly blocks rollout while everyone assumes somebody else is resolving it.
Against the business case that funded the work, not against usage counts. Logins prove nothing. We instrument the decisions the AI touches, the time or error rate it changes, and the cost it incurs — so the case can be defended, or honestly withdrawn, at the next budget round.
Which decisions AI is permitted to touch and which it is not; acceptable-use boundaries; human oversight and escalation with named owners; data handling and retention rules; evaluation and quality ownership; and audit logging. It is usually a short document, and writing it is usually what unblocks the rollout.
With role-based enablement built around your actual systems and data, rather than a generic course. Engineers, analysts and operations staff need different things, and the useful content is nearly always about where the tool is unreliable — not about how to write a better prompt.
A first call is technical, not a sales call. Bring a problem, an architecture, or a stalled initiative.
30 minutes, no pitch deck.