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Keep AI performing after go-live
Models change, data drifts and processes evolve. We monitor, evaluate, improve and extend your AI in production — under agreed service levels and governance.
Last reviewed: 29 September 2026
Agreed
accuracy and availability thresholds, reported monthly
Monthly
value and adoption report
Every quarter
new or improved use cases delivered
Service levels are defined per engagement.
Go-live is the start, not the finish
AI systems do not stay still. Vendors update models, sometimes changing behaviour overnight. Data and documents change. Processes shift, users find new edge cases, and costs grow with usage. When the project team moves on, nobody owns quality, cost or evidence for auditors — and value reporting quietly stops.
- No clear owner for AI after the project ends
- Vendor model updates change behaviour without warning
- Value and cost reporting stops after launch
AI needs to be operated like any other critical service.
Service scope
| Service | What it covers |
|---|---|
| Monitoring and incident response | Availability, errors and unusual behaviour, with defined response times |
| Evaluation and quality regression | Scheduled and change-triggered testing against evaluation sets |
| Cost and usage optimisation | Model and usage costs per use case, with optimisation actions |
| Governance evidence | Logs, test results and reviews maintained for audits |
| Adoption support | Help for users, feedback loops and targeted enablement |
| Enhancement backlog | Quarterly roadmap of improvements and new use cases |
Choose the level of support
Advanced — Everything in Essential, plus change-triggered evaluation, cost optimisation, governance evidence packs and a quarterly enhancement allowance.
Outcome-linked — Advanced scope, with part of the fee tied to agreed business KPIs such as throughput, accuracy or savings.
Continuous governance, not annual review
- Changes to models, prompts and agents follow an approval process
- Evaluation results recorded before and after every change
- Access and permissions for agents reviewed regularly
- Incidents logged, analysed and reported with corrective actions
Already running AI in production? Let’s talk about keeping it reliable.
See it in practice
Client Zero
Signal-based GTM automation
How we operate our own multi-step AI workflows across several systems, with monitoring, cost control and human approval points.
Read the story
How it works
1
Onboarding
2–4 weeks
Inventory of AI in scope, baselines, runbooks and evaluation sets.
2
Steady state
Monitoring, evaluation, reporting and support to agreed service levels.
3
Quarterly review
Value, cost and risk review with an agreed improvement roadmap.
Commercial options: monthly retainer · optional outcome-linked component.
Questions about AI managed services
Can you operate AI that you did not build?
Yes. Onboarding includes a review of each system’s design, data, controls and documentation. Where gaps exist, we agree a short remediation plan before taking on service levels.
What service levels do you offer?
Service levels cover response times, availability monitoring, evaluation frequency and reporting. They are agreed per engagement based on how critical each use case is.
How do outcome-linked fees work?
We agree a small set of business KPIs and a baseline. Part of the monthly fee then varies with performance against those KPIs. This works best where results can be measured reliably and are not driven mainly by outside factors.
How do you handle vendor model updates?
We track announced changes, test new model versions against your evaluation sets before switching where possible, and monitor behaviour closely after any change.
Put your AI on a service footing
- 30 minutes with a senior lead
- No sales script — a working conversation
- A clear view of where AI can pay back first