AI Managed Services
An AI system can be fully available and completely wrong. Standard infrastructure monitoring will report green throughout, because the endpoint is responding and the latency is fine.
Our AI managed service is built around the failure modes that actually occur: quality regression, data drift, retrieval decay as the corpus ages, cost escalation, and provider model changes that alter behaviour without warning.
Platforms we typically deliver this on
Scope of the service
Output quality monitoring
Continuous evaluation against maintained test sets, with alerting on regression rather than only on errors.
Drift detection
Monitoring input distributions and model performance to catch degradation before users report it.
Retrieval maintenance
Re-indexing, supersession handling and retrieval quality measurement as the underlying corpus changes.
Cost management
Token and inference spend monitoring, model routing, caching and per-team budgets with alerting.
Model and prompt lifecycle
Versioning, staged rollout, A/B evaluation and rollback for every change, including provider-side model updates.
Incident response
24/7 response with defined severity levels, including a documented path to disable an AI feature quickly and cleanly.
Where this is used
Representative engagements, described at the level our clients permit. Sector and shape are accurate; identifying detail is withheld.
Keeping a public-facing assistant accurate as policy changes
Policy updates monthly. The service re-indexes on change, runs the evaluation suite against updated content, and flags answers that shifted materially for human review before release.
Outcome — Answers stay current without a manual re-verification project each cycle.
Containing inference spend
Costs were rising faster than usage. Routing, caching and per-team quotas with alerting brought spend under control and made it attributable.
Outcome — Spend predictable and owned by the teams generating it.
Managing a provider model upgrade
A hosted model was deprecated with limited notice. We evaluated candidate replacements against the client's own test suite and migrated with measured quality comparison.
Outcome — Forced migration handled without a quality regression reaching users.
Before you get in touch
Will you run AI systems you did not build?
Yes. Transition includes a technical review, and we will tell you plainly what needs remediation before we can commit to service levels.
What service levels apply to quality?
We agree evaluation metrics and thresholds during transition, then report against them monthly alongside availability and response times.
How is this priced?
A fixed monthly fee based on the number of systems, environments and coverage window, with inference costs passed through at cost.
Often engaged alongside this
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Read moreStart with three weeks and a straight answer
The AI Readiness Assessment is fixed in scope, fixed in price and produces four deliverables you own — whether or not you continue with us.