AI Managed Services and Retainers
Running your AI systems after launch — monitoring, retraining, evaluation and incident response against agreed service levels — with a written exit path, because a managed service you cannot leave is a dependency rather than a service.
Most AI systems are built by people who then leave, and degrade quietly afterwards because nobody owns them. A managed service is worth having when it removes that problem — and worth avoiding when it replaces one dependency with another you like slightly better.
AI managed services provide ongoing operation of production AI systems: monitoring for drift and degradation, scheduled and triggered retraining, evaluation of model and prompt changes, cost management, and incident response — against defined service levels rather than best efforts.
What is actually covered
| Area | What we do | What we measure |
|---|---|---|
| Monitoring | Drift, performance, segment health, cost | Time to detect a degradation |
| Retraining | Triggered by evidence, with evaluation gates | Model performance against baseline |
| Evaluation | Suites run on every change; provider drift watched | Regressions caught before release |
| Cost | Attribution, anomaly alerts, optimisation review | Cost per request and per feature |
| Incidents | Defined response, rollback, root cause | Time to respond and to restore |
| Reporting | What changed, what it cost, what needs deciding | Delivered on a fixed cadence |
Service levels, not best efforts
A managed service without numbers is a retainer with good intentions. Response and restoration targets, monitoring coverage, retraining triggers and reporting cadence are all written down, along with what happens when we miss them. If a supplier will not put numbers in the agreement, the service is not defined.
How we scope it honestly
Start from what actually needs operating
Some systems need weekly attention and some need a quarterly look. Scoping every system at the highest tier is how managed services become expensive without becoming more useful, so we tier by consequence and change rate rather than applying one level across the estate.
Take on systems we did not build
This is a common and legitimate case. We assess what exists, document it properly — which is frequently the first time that has happened — and take over operation. Where something needs remediation before it can be operated safely, that is a separate piece of work with its own price, named before the retainer starts.
Keep your team in the loop, not out of it
Your named service owner sees the same monitoring we do, receives the same reports, and participates in incident reviews. A managed service that operates as a black box removes the problem it was hired to solve and creates a worse one.
Write the exit at the start
What is handed over, in what form, over what period, and what it would take for your team to run it. Agreed at the beginning while everyone is reasonable, rather than negotiated during a transition when nobody is.
Report what needs deciding, not just what happened
Reporting that lists activity is a timesheet. Reporting that says a model has degraded to the point where retraining will not fix it, or that a feature's cost per request has doubled and here are the options, is the thing worth paying for.
When you should not buy this
- When one system needs occasional attention. A support agreement with hours is cheaper and more honest than a managed service.
- When you have the capability and the capacity. We would rather help you build the practice — see MLOps implementation — than run it indefinitely.
- When the real problem is that the system was built badly. Operating a broken system costs more than fixing it. See AI project rescue.
- When nobody on your side will own the relationship. Managed services without a client-side owner drift into invisibility and then into cancellation.
How the engagement runs
The exit path is written before the service starts, not when it ends.
Assessment and onboarding
Systems assessed and documented; monitoring coverage, gaps and any remediation needed identified and priced separately.
Service definition
Tiers by consequence, service levels, retraining triggers, incident process and reporting cadence agreed and written.
Transition
Monitoring and alerting brought under management; runbooks written or corrected; first incident rehearsal.
Operation
Monitoring, retraining, evaluation, cost review and incident response against the agreed levels.
Review
Whether the service is still the right shape, including whether you should take it in-house.
What you receive
Systems that stay working, reporting that supports decisions, and a way out whenever you want one.
Service definition
Tiers, service levels, triggers and escalation, written with numbers rather than intentions.
Monitoring and alerting
Under management, visible to your named owner as well as to us.
Retraining and evaluation
Triggered by evidence, gated on performance against baseline.
Cost management
Attribution, anomaly alerts and a periodic optimisation review.
Incident response
Defined process, rehearsed, with root cause reported rather than described.
Exit plan
What is handed over, in what form, over what period — agreed at the start.
Is this the right engagement?
Worth being direct. AI Managed Services and Retainers is the wrong spend in some situations, and those are listed rather than buried.
Good fit if
- Production AI systems have no clear owner after the build team left.
- Degradation would not be noticed until a business metric moved.
- Retraining happens when someone remembers, or not at all.
- AI costs move and nobody can explain why.
- You need defined response times rather than best efforts.
Choose something else if
- One system needing occasional attention — buy support hours instead.
- You have the capability and want to build the practice. See MLOps implementation.
- The system needs fixing rather than operating. See AI project rescue.
- Nobody on your side will own the relationship.
Frequently asked questions
Marked up with FAQPage schema so these answers can surface directly in search results and inside AI assistant responses.
What does an AI managed service actually cover?
Monitoring for drift and degradation, retraining triggered by evidence rather than by calendar, evaluation of model and prompt changes before release, cost attribution and optimisation, and incident response — all against written service levels. If a supplier will not put response and restoration numbers in the agreement, the service is not defined.
Can you operate systems you did not build?
Yes, and it is a common case. We assess what exists and document it — frequently for the first time — then take over operation. Where something needs remediation before it can be operated safely, we price that separately and name it before the retainer starts rather than absorbing it invisibly.
Are we locked in?
No, and the exit plan is written at the start rather than negotiated at the end: what is handed over, in what form, over what period, and what your team would need to run it. A managed service you cannot leave is a dependency, and we would rather lose the renewal than hold you through opacity.
Should we do this in-house instead?
Often yes, and we will say so. If you have the capability and the capacity, building the practice is better value than paying us indefinitely — that is what MLOps implementation is for. Managed services earn their place when the capability genuinely is not there and building it is not the priority this year.
How is this different from a support agreement?
A support agreement responds when you raise something. A managed service watches, acts before you notice, retrains on evidence and reports what needs deciding. For a single stable system, support hours are cheaper and more honest — we will point you there rather than sell the larger thing.
Often paired with this
Most clients combine two or three engagements from the Managed AI Services and Talent pillar. These are the ones that most often run immediately before or after.
Model Maintenance and Retraining
Retraining on evidence, gated against the incumbent, with the honest call when retraining will not fix it.
Read more →24/7 AI Support and SLA
Real service levels with severity by business impact — and a straight answer when business hours would do.
Read more →Dedicated AI Development Teams
A complete team with its own lead and your product ownership, plus a transition plan from day one.
Read more →Is this the right engagement?
Tell us what you are trying to build. If a different service fits better, or if you do not need us at all, we will say so.