24/7 AI Support and SLA
Support for production AI systems with response and restoration targets you can hold us to — and an honest conversation about whether you actually need cover at three in the morning.
Round-the-clock support costs a great deal and most AI systems do not need it. The useful conversation is about which systems genuinely fail in ways that cannot wait until morning, and covering those properly rather than covering everything thinly.
AI support with service level agreements provides defined response and restoration commitments for production AI systems, with severity classified by business impact, escalation paths, and cover extending to the specific failure modes AI systems have.
Severity by business impact, not technical category
| Severity | Definition | Example |
|---|---|---|
| Critical | Business operation stopped or actively harmed | Wrong decisions being issued to customers at volume |
| High | Significant impairment with no workaround | Model unavailable; a queue building with no manual route |
| Medium | Degraded with a workaround | Accuracy fallen but review is catching it |
| Low | Minor or cosmetic | Latency slightly above target at peak |
AI systems have a severity class ordinary support does not
A model that is available, fast, returning no errors and confidently wrong is a critical incident that every conventional monitoring dashboard reports as healthy. Support for AI has to cover output quality, not just uptime — and severity has to be classifiable on that basis or the worst failures never get raised.
What cover actually includes
The AI-specific failure modes
Model unavailability and latency, yes — and also accuracy degradation, drift alerts, a provider changing a model beneath you, guardrail failures, runaway cost, agents looping or exceeding limits, and retrieval returning nothing. These are the incidents that matter and they need naming in the agreement to be covered by it.
Defined response and restoration targets
Time to acknowledge, time to respond with a human who can act, and time to restore or provide a workaround, per severity. Plus what happens when we miss them, because a target with no consequence is a statement of hope.
Rollback as the first response, usually
For most AI incidents, reverting to the previous model or prompt version restores service faster than diagnosing the cause. Diagnosis follows restoration rather than delaying it, which requires that rollback was built to work — see model deployment and serving.
A path to someone who understands the system
Escalation to an engineer who knows your architecture, not a script. This is the difference between support that resolves an AI incident and support that acknowledges it politely for four hours.
Root cause after the fact
Written up, with the fix that prevents recurrence and whether it is in scope or needs separate work. Incident volume that never falls indicates a system needing remediation rather than support, and we will say so.
Do you need 24/7?
- Ask what breaks overnight and who notices. A batch process failing at 2am that nobody consumes until 9am does not need waking anyone.
- Ask what a wrong output costs per hour. Customer-facing decisioning at volume is a different case from an internal analytics model.
- Ask whether your team is already on call. If they are, extended-hours cover with escalation may suffice.
- Consider tiering. Round-the-clock for the two systems that genuinely need it, business hours for the rest, is usually the right shape and materially cheaper.
Most organisations that ask for 24/7 across the estate need it for a small subset. We will propose the smaller thing, because support nobody uses at night is an expensive way to feel covered.
How the engagement runs
Which systems genuinely need overnight cover is established before the agreement is written.
Assessment and tiering
Systems assessed for overnight impact; cover tiered rather than applied uniformly.
Agreement
Severity definitions including output-quality failures, response and restoration targets, escalation and miss consequences.
Onboarding
Access, monitoring integration, runbooks, rollback verified to work, and an incident rehearsal.
Support
Cover to the agreed levels, with incidents logged and root causes written up.
Review
Incident patterns, target performance, and whether cover is the right shape.
What you receive
Support you can hold to numbers, covering the failures AI systems actually have.
Tiered cover
Round-the-clock where warranted, business hours elsewhere, with the reasoning documented.
Severity definitions
By business impact, explicitly including confidently-wrong-output failures.
Response and restoration targets
Per severity, with consequences for missing them.
Verified rollback
Tested during onboarding so restoration is available before it is needed.
Incident reports
Root cause with the preventive fix named and its scope stated.
Quarterly review
Patterns, performance and whether the shape still fits.
Is this the right engagement?
Worth being direct. 24/7 AI Support and SLA is the wrong spend in some situations, and those are listed rather than buried.
Good fit if
- Customer-facing AI operates outside business hours.
- A wrong output at volume would be expensive within hours.
- Your team has no out-of-hours capacity for AI systems.
- Contractual commitments to your own customers depend on it.
- An incident occurred and the response was slower than acceptable.
Choose something else if
- Nothing breaks overnight in a way anyone notices before morning.
- You want operation and improvement, not incident response. See managed services.
- The systems need remediation rather than support. See project rescue.
- No monitoring exists, so incidents would be reported by users rather than detected.
Frequently asked questions
Marked up with FAQPage schema so these answers can surface directly in search results and inside AI assistant responses.
What counts as an AI incident?
The obvious ones — unavailability, latency, errors — and the ones conventional support misses: accuracy degradation, drift, a provider changing a model beneath you, guardrail failures, runaway cost, agents looping. A model that is available, fast and confidently wrong is a critical incident that every uptime dashboard reports as healthy.
Do we really need 24/7 cover?
Usually for a subset rather than the estate. The test is what breaks overnight, who notices before morning, and what a wrong output costs per hour. Tiered cover — round-the-clock for the two systems that need it, business hours for the rest — is generally the right shape and materially cheaper.
What are the response times?
Agreed per severity and written into the agreement, along with what happens when we miss them. A support agreement without numbers and without consequences is a statement of intent. We would rather commit to targets we can hold than to an impressive-sounding figure we cannot.
What happens first in an incident?
Usually rollback. For most AI incidents, reverting to the previous model or prompt version restores service faster than diagnosing the cause, so diagnosis follows restoration rather than delaying it. That requires rollback to have been built to work, which we verify during onboarding rather than during the first incident.
Can you support systems you did not build?
Yes. Onboarding covers assessing the architecture, verifying that rollback works, integrating monitoring and writing runbooks — frequently the first time that has been done. Where a system cannot be supported safely as it stands, we say what needs fixing rather than accepting an agreement we could not honour.
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.
AI Managed Services and Retainers
Ongoing operation against defined service levels, with a documented exit to your own team whenever you want it.
Read more →Model Maintenance and Retraining
Retraining on evidence, gated against the incumbent, with the honest call when retraining will not fix it.
Read more →AI Project Rescue and Recovery
An independent read on a stalled project: what is salvageable, what is not, and whether to continue at all.
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.