AI operations as a managed service, with commitments instead of intentions.
Operations at NexPatch covers monitoring, model maintenance, cost control and ongoing tracking against regulatory duties for AI systems already running in production. We work to a general operating target of 99.9 percent availability - not an individual guarantee per customer - with response times staggered by the severity of an incident.
Why AI projects fail after go-live
A system that works at rollout does not automatically still work as well a year later. Three reasons explain most failures after go-live.
Underlying data sources change, formats shift, or new exceptions appear that were unknown at the original build. A system trained and connected on last year's data drifts further from your company's reality over time if nobody keeps the connection current.
Without ongoing control, compute load rises unnoticed and budgets run away. As usage grows, so does the need for compute, and without something watching that relationship, the cost trend only becomes visible once the invoice has already risen.
After a project closes, often nobody is clearly responsible any more for monitoring the system, until a failure exposes the gap. A project team disbands, responsibility formally moves to the IT department, which has its own priorities and does not know the AI system in the same detail as the people who originally built it.
Operations as a standalone service addresses all three from the start, with clear ownership instead of a gap that only surfaces after a failure.
What we take over in ongoing operations
We continuously watch the quality of results and the technical availability of the system, rather than reacting only when something looks off. That includes regular reporting you can actually follow, so you learn the state of your system on a schedule, not only when an incident happens.
We update, review and, where useful, replace the language models in use when a newer one suits your case better. A replacement never happens unreviewed - only after a test phase where the new model is measured against your own requirements, not against general benchmark scores.
We keep an eye on compute load and tell you before a cost trend becomes a surprise. If usage grows faster than expected, we raise it early instead of surprising you with the invoice at the end of the month.
We continuously check your system against the applicable duties under the EU AI Act, GDPR and sector-specific requirements, not just once at rollout. If a deadline or a requirement changes - because growing usage moves your system into a different risk category, say - we track that and update the documentation accordingly.
Service tiers at a glance
| Service tier | Best fit | Response time |
|---|---|---|
| Basic | Internal tools with low impact if they fail | Set in the contract |
| Extended | Productive systems in regular use | Set in the contract |
| Critical | Systems with a direct effect on customer-facing business | Set in the contract |
We deliberately don't name a figure here. A general response time would have to look different for an internal tool than for a system your customer-facing business depends on, and one figure for both would be wrong for both. We set the deadline per service tier in the operations contract. How the severity levels are defined, when the commitment applies and what happens if a deadline is missed is on the Service Level page →.
What working together looks like in ongoing operations
Operations with us is not an anonymous queue of support tickets. Every contract has a named contact who knows your system and your use case, with a deputy for when they are away. At intervals matched to your service tier, we summarise the state of the system in a short, readable report rather than handing you raw data unfiltered. Bigger changes - a model switch or an adjustment to the approval logic, for example - we discuss with you in advance rather than making them unilaterally.
What you keep, no matter what
Even during ongoing operations, the rights to your data, your models and your configuration stay with you. You keep access to your system, to the logs and to the underlying model weights at all times. That holds regardless of how long the relationship has been running - there is no point at which these rights quietly transfer to us. Operations with us is therefore not a contract that binds you to us, but a service you can end at any time under the agreed terms.
This separation between service and ownership is deliberate, because we assume trust builds over time rather than being forced through contractual lock-in. A customer who could leave at any time but doesn't is, to us, more reliable proof that our work is worth something than any long notice period could ever be.
What operations costs
Billing is based on a monthly fee that depends on service tier, the number of systems supported and the agreed response times. Unlike billing per request, this amount stays predictable across the month, regardless of how heavily a system is used on any given day. The exact amount is only fixed in writing after the assessment. Price frames for orientation are at Pricing →.
How you leave again
An operations contract with us is cancellable monthly after an agreed minimum term. The full exit process, including what format you receive your data and models in and what it costs, is at Switching AI provider →.
When it pays to move into operations
Not every company needs operations from day one. If you are just testing a first pilot, you can run it for a while without a separate operations contract. But once a system moves into the productive rollout and gets used by staff in daily work, the risk of an unsupported system shifts noticeably, because a failure then no longer affects only an internal test scenario - it interrupts a real workflow. Most of our customers therefore commit to an operations contract at the latest when moving from pilot to rollout, rather than waiting until after the first serious incident, when the damage is already done.