Keep Operational AI reliable, measurable, and improving.
Continuous AI Operations extends Operational AI beyond launch. After agents and workflows are live, we monitor performance, evaluate outputs, tune systems, and guide expansion so AI continues creating value 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.
- Production monitoring with defined reliability, performance, and cost thresholds
- Evaluation coverage for model, prompt, context, and workflow changes
- Ongoing prompt, context, and workflow tuning as your business evolves
- Model upgrade path with benchmarking, migration, and re-evaluation
- Incident response with a named engineer and documented runbooks
- Monthly operational reviews and expansion roadmap
How Continuous AI Operations engagements start
A structured process to define system ownership, observability, and ongoing operational support.
Operational AI review
We review the agents and workflows currently in production, what they do, who owns them, how they were built, and where reliability or adoption risk exists.
A clear view of operational scope, risk, and ownership.
Monitoring and evaluation baseline
We wire up monitoring, logging, evaluation coverage, and performance baselines across accuracy, latency, cost, and adoption.
A dashboard and operating baseline your team can trust.
Continuous operations
A dedicated engineer monitors, tunes, upgrades, and responds while your team focuses on the business.
Operational AI systems that keep improving instead of degrading.
Which production AI system would create the most risk if performance quietly degraded?
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 Continuous AI Operations?
Monitoring, evaluations, prompt and context tuning, model upgrades, incident response, and monthly roadmap reviews for systems already in production. New workflows or agents are scoped separately.
Can you support 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
- One or more production AI workflows or agents with real operational dependency
- No dedicated internal AI operations capability
- Need monitoring, tuning, evaluation, and incident response
- Willing to invest in operational ownership to protect business outcomes
Not a fit
- Pre-production AI experimentation
- Looking for implementation rather than operations support
- Staff augmentation needs
- No access or operational ownership model
Scope operational support for your production AI systems
In 20 minutes, we'll review your production systems, operational risks, support needs, and the right ongoing ownership model.