AI operations - managed
AI managed services
An AI system in production is not a deliverable. It is a running service that drifts, costs money every hour, and fails in ways a test suite never sees.
The pattern is familiar. A build lands, a demo goes well, the project team disperses, and four months later nobody can say whether the thing is still accurate. Managed AI services exist because the operating phase has real work in it and no natural owner inside most organisations.
What actually needs operating
- Evaluation runs. The evaluation set gets rerun on every prompt change, model version change and data source change. Results are stored so a regression is visible rather than discovered by a customer.
- Drift watch. Refusal rate, error rate, latency, escalation rate to human review, and output length distribution. A shift in any of these usually precedes a complaint.
- Cost control. Spend per workflow against a ceiling, with alerting. Runaway agent loops and prompt bloat are the two most common causes of a surprise bill.
- Provider changes. Model deprecations, terms changes and pricing changes arrive on the vendor's schedule. Somebody has to read the notices and run the regression.
- The human queue. Cases the system flagged as uncertain need clearing, and the corrections need feeding back as evaluation cases.
- Incidents. When a vendor has an outage or changes a response shape, the fallback path needs to have been tested rather than merely documented.
What a sensible retainer covers
A defined response time for incidents, a scheduled evaluation run with the results reported, a monthly cost report against the ceiling, and a fixed allocation of engineering hours for changes. What it should not be is an open-ended body-count arrangement, because that rewards the wrong thing. If a system needs continuous heavy engineering after month three, either the scope was wrong or the build was.
Why building and operating in the same place matters
The team that wrote the retrieval pipeline knows why the chunking is set the way it is. Handing operations to a separate group means either extensive documentation nobody reads or a slow rediscovery of decisions. Digiton builds and then operates what it builds, across production deployments in 8 countries, which is also why the build decisions lean conservative: we carry the on-call.
Handover is a valid outcome
The goal is not permanent dependency. A good managed engagement ends when the client's own team can take it over, and the contract should say so: you own the prompts, the evaluation sets, the retrieval index and the documentation. Anything that makes leaving hard is a warning sign in a vendor, including this one. A short AI audit is the usual way to start, and it is deliberately small.
Frequently asked questions
What are AI managed services?
Ongoing operation of an AI system after it goes live: scheduled evaluation runs, drift monitoring on refusal and error rates, cost control against a ceiling, handling model deprecations and terms changes, clearing the human review queue, and incident response when a provider changes behaviour or goes down.
Why not have our own team operate it?
Often they should, and a good engagement ends in exactly that handover. The reason to start with a managed arrangement is that the team who built the system knows why each decision was made, and the alternative is either documentation nobody reads or a slow rediscovery of the same choices.
What should an AI retainer include?
A defined incident response time, a scheduled evaluation run with reported results, a monthly cost report against an agreed ceiling, and a fixed allocation of engineering hours for changes. Avoid open-ended arrangements billed by headcount, since they reward the wrong outcome.
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