Built is the easy half.Run is the hard half.
Most AI projects don't fail at launch. They fail four months later, quietly — when the model drifts, edge cases pile up in a queue nobody watches, and the cost per call triples without anyone noticing. Managed AI Services is the discipline that stops that, on a monthly retainer with a named person accountable for each workflow.
Six things we watch.
Each of these is a failure mode we have seen take down a working system. None of them announces itself.
Monitoring & evaluation
Continuous evaluation against a held-out set that reflects your real inputs, with alerts when quality moves rather than when it collapses. You hear it from us, not from a customer.
Drift detection & retraining
Input distributions shift as your business changes. We measure the shift, retrain against it, and version every change so you can roll back to a known-good state.
Cost & capacity control
Per-workflow cost tracking with hard ceilings, routing to smaller models wherever they perform identically, and a line-by-line monthly account.
Human review queues
A defined confidence floor below which work goes to a person. We design the queue, define the escalation path, and report on what it caught — the catches are the evidence the control works.
Incident response
A named engineer, a defined response window, a written post-incident record. AI systems fail silently and plausibly; the runbook accounts for that.
Named accountable owner
One person owns each workflow, documented so it maps onto the governance you already have — an SM&CR responsibility map, a DPIA, or a board report.
Our agents, working.
A live view of the fleet we operate — throughput, latency, extraction accuracy, and the escalations deliberately routed to a human. Amber is a person, every time it appears.
What lands in your inbox each month.
The monthly report is the product. If it is not readable by someone who was not in the room, it is not doing its job.
- Volume processed, by workflow, against the previous period
- Quality against the held-out evaluation set, with any movement explained
- Every escalation that went to a person, and what the review found
- Model or prompt changes made, versioned, with rollback points
- Cost per workflow against the agreed ceiling
- Incidents, response times and written post-incident records
- Advance notice of anything about to change
- The named owner for each workflow, and any change to it
Common questions.
Do we have to take a retainer?
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No. You own the system outright and can operate it yourself — the handover includes the runbook, the test suite and the evaluation harness precisely so that is a real option. Most clients take the retainer because monitoring an AI system properly is a specialist job, not because they are locked in.
What does the confidence floor actually do?
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Every workflow has a threshold below which output is not used automatically. Under it, the item routes to a named person with the source material attached. We report on what the floor caught, because those catches are the evidence the control is working rather than decorative.
How do you stop costs running away?
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Per-workflow tracking with hard ceilings, and routing to smaller models wherever they perform identically on your evaluation set. The monthly account is line-by-line, so a cost change is visible in the month it happens rather than at renewal.
We can help.
Tell us what's hard, expensive, or taking too long — and we will help you identify, optimise and deploy AI for innovation, efficiency and growth.