Keep your AI performing and improving.
After launch, a named Metacto lead operates, evaluates, and improves your production AI systems, and reports quality, cost, adoption, and incidents to you every month.
Northline Supply · Production AI
Service owner: Managed AI lead · Monthly review, Oct 6
- Renewal briefs
- Invoice triage
- Support routing
Quality · eval pass rate
96.4%+1.1 pts
Cost per run
$0.042
Down 8% vs Aug after prompt caching
Adoption · weekly users
184/ 210
Incidents
3opened 3resolved
Median time to resolve: 5h 40m
- 1Add contract-amendment cases to the renewal eval setManaged AI leadIn progress
- 2Route low-confidence invoices to the AP reviewerAI engineerScheduled
- 3Approve model upgrade for support routingYour system ownerDecision needed
Found, fixed, and reported the same day.
Production AI fails quietly. A field disappears upstream and the output gets worse without an error. The service is built to catch that in monitoring, not in a customer complaint.
- Caught by evaluation, not by users
- Nightly quality checks run on real examples, so degraded output raises an alert even when nothing technically fails.
- Fixed at the cause
- The fix goes where the problem started, and new regression cases keep it from coming back.
- Your owner approves the release
- Material changes ship after your system owner signs off, and every incident is reported at the next review.
Severity
2 · degraded output
Owner
Managed AI lead
Time to resolve
6h 18m
- 09:12Alert
Detect
Quality check flags renewal briefs
Nightly evaluation: contract-end date missing in 14 of 62 briefs. Pass rate drops from 97% to 81%.
- 09:47Cause found
Trace cause
CRM schema change renamed a field
Retrieval reads contract_end, now renewal_date. The brief is built with the field empty.
- 13:0548 / 48
Fix and test
Mapping updated, three regression cases added
Field mapping fixed and a schema check added. Regression suite: 48 of 48 pass.
- 15:30Approved
Release
Released after your system owner approves
Affected briefs regenerated. Customer success notified in the team channel.
- Oct 6Closed
Review
Reported at the monthly review
Cause, fix, new checks, and 6h 18m to resolve. Schema alerts added for the other two systems.
Production AI with an owner, not a side job.
- 01
Problems caught early
Drift, failures, and cost spikes show up in monitoring and evaluation before your team starts working around them.
- 02
One accountable lead
A Managed AI lead owns reliability and the improvement roadmap, with engineers who know your systems.
- 03
Improvement on evidence
Each month's priorities come from measured quality, cost, adoption, and results, and you approve them.
Coverage and response, in writing.
Every engagement has a service plan you can inspect. It names what we are responsible for, when, and how fast we respond. Targets are set by business criticality, not a generic tier.
The plan sets
- Covered systems and integrations
- Support hours and escalation contacts
- Response and resolution targets by severity
- Quality, latency, and cost standards
- Included engineering capacity each month
We operate
- Monitoring, logging, and alerting
- Quality evaluation on real examples
- Model, vendor, and integration maintenance
- Incident ownership, from cause to tested fix
We report and improve
- Monthly operating report
- Usage and cost by system
- Prioritized improvement roadmap
- Quarterly business review
Major new builds are scoped separately. Systems we did not build are accepted after a technical assessment and an agreed transition. Pricing follows responsibility, coverage, and capacity, not agent count. Cloud and model usage bills to your own accounts.
Assessed, baselined, then run against the plan.
We start from the AI systems you already run. Your system owner joins each review.
Technical assessment
We review what is in production, who owns it, how it was built, and where the risk is.
Agreed scope, risks, and the service plan.
Baseline
We set up monitoring, logging, and evaluations across quality, latency, cost, and adoption.
A performance baseline your team can see.
Operate
We monitor, maintain, respond to incidents, and deliver roadmap improvements.
Systems kept reliable as the business changes.
Review
We report results against the plan and reset priorities with you.
An operating report and updated roadmap.
Common questions
What are the service levels?
They are set in the service plan for each system by business criticality: support hours, response and resolution targets by severity, and quality, latency, and cost standards. We report against them every month.
Can you support systems Metacto didn't build?
Often. We run a technical assessment first. If the architecture is supportable, we agree a transition plan before taking responsibility.
How does our team stay involved?
Your system owner sets priorities with us, approves material changes, and joins each review. Your team keeps using the system; we keep it working.
What access do you need?
Scoped access to the systems in scope, agreed with IT. Your AI systems keep running in your own cloud, inside your security boundary.
What happens if we stop the service?
You retain the deployed system and documentation and can keep operating it through your own accounts after a documented handoff. What ends is Metacto's monitoring, maintenance, and improvement.
Managed AI Services
Give your AI systems an accountable owner.
45 minutes with our team. We look at what your AI systems produce now, where they are drifting, and what a service plan would cover.
Your request is received.
Choose a time for your 45-minute session now, or our team will follow up to arrange it.
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