Keep production AI reliable, measurable, and improving.
Continuous AI Operations extends Enterprise Context Engineering beyond launch, providing the monitoring, evaluation, tuning, and operational support required to keep production agents and workflows performing as your business evolves.
Monitoring → evaluation → tuning → continuous improvement
Built for teams with production AI systems that need operational ownership without building an internal AI operations function.
Runs in your cloud · SOC 2-ready audit · Engineer-on-call during business hours, paged after-hours
Why production AI systems degrade without operational ownership
Production AI needs active monitoring, maintenance, and continuous optimization to stay reliable.
Outputs drift silently
A system that passed eval in week 1 starts producing subtly worse results by month 3. Nobody notices until a customer does.
AI infrastructure evolves constantly
Models, APIs, vendor capabilities, and costs change continuously. Without active ownership, production systems fall behind.
Context goes stale
Your CRM, docs, and playbooks change weekly. The agent's context layer needs to change with them, or the outputs stop matching reality.
No clear owner
Without ownership, production AI becomes a reactive burden for whoever notices the problem first.
What Continuous AI Operations delivers
Operational ownership for production AI systems—so reliability improves instead of slowly degrading.
- 24/7 monitoring with explicit SLOs on latency, accuracy, and cost
- Evaluation runs on every prompt, context, or model change — no silent regressions
- Ongoing prompt and context tuning as your business data and playbooks evolve
- Model upgrade path: benchmarks, migration, and re-eval across Claude, GPT, Gemini
- Incident response with a named engineer and a documented runbook
- Monthly review with the business owner: what shipped, what to scope next
How Continuous AI Operations engagements start
A structured onboarding process to define system ownership, observability, and operational support.
Discovery and handoff
Walk the systems currently in production — what they do, who owns them, how they were built, what's broken today. If metacto built them, we already have the context. If not, we scope an onboarding phase.
A clear picture of scope, risk, and what the retainer covers on day one.
Instrumentation and eval baseline
We wire up monitoring, logging, and evaluation harnesses. Establish a baseline of accuracy, latency, and cost per workflow. Document the failure modes we'll alert on.
A dashboard you trust and an alerting contract we both agree to.
Live retainer
Dedicated engineer, private Slack channel, weekly async updates, monthly in-person reviews. We tune, upgrade, and respond while you focus on the business.
AI leverage that compounds instead of decays.
Which system are you most worried about silently degrading? Let's scope a retainer.
Continuous AI Operations in practice
See how operational monitoring and evaluation prevented production drift before it became a business problem.
Mid-market B2B SaaS, 3 agents in production
The problem
Nine months after shipping their renewal-brief, deal-brief, and support-triage agents, the CS team started quietly going back to manual work on complex accounts. A silent quality regression — tied to a CRM schema change nobody flagged — was producing briefs that missed a key field.
The outcome
Retainer engineer detected the drift in the weekly eval run, traced it to the schema change, updated the context layer and eval suite, and shipped a fix inside five business days. Accuracy back above the SLO in the next weekly report.
Common questions
What's covered by the retainer?
Monitoring, evaluations, prompt and context tuning, model upgrades, incident response, and monthly roadmap reviews for systems already in production. New workflows or new agents are scoped as separate builds — the retainer doesn't become a stealth dev team.
Can you run systems metacto didn't build?
Sometimes. If the system architecture is compatible and the implementation meets operational standards, we can scope an onboarding phase before assuming support ownership.
What are the SLAs?
SLOs are set per engagement based on the workflow's business criticality. Typical shape: 99.5% uptime, p95 latency targets per workflow, accuracy floor re-benchmarked monthly, 1-business-hour response for sev-1 during business hours.
How is it priced?
Continuous AI Operations is priced as a fixed monthly operational retainer based on system complexity, support scope, and reliability requirements.
Where does it run?
Your cloud. We operate inside your perimeter, with your auth, your data, your tools. The retainer is an operations engagement, not a hosted service.
Can we end the retainer?
30 days' notice after the minimum. We leave behind the monitoring, evals, runbooks, and documentation your next team needs to take over.
Is this the right fit?
Good fit
- At least one agent or workflow in production with real business dependency on it
- No dedicated internal AI platform team, and no plan to hire one soon
- Willingness to invest in monitoring and evals as part of the retainer
- Budget for a monthly retainer vs trying to staff the function internally
Not a fit
- Pre-production systems (we build those; this retainer is for run)
- Looking for a staff-augmentation contract by the hour
- Cannot grant the access needed to operate inside your environment
- Want metacto to take full autonomy for business-critical decisions (we operate with human-in-the-loop)
Scope operational support for your production AI systems
30 minutes with a CTO to assess your production systems, operational risks, support needs, and the right ongoing ownership model.