Your AI systems, run by the team that built them.
Production agents and workflows don't stay production-grade on their own. Models drift, prompts rot, context shifts, new failure modes appear. We monitor, evaluate, tune, and upgrade the systems we shipped — so your team keeps getting leverage, not a second job.
Monthly retainer · quarterly reviews · dedicated engineer on-call.
Built for teams running MetaCTO-built (or compatible) AI systems in production who need ongoing operations, not another internal hire.
Runs in your cloud · SOC 2-ready audit · Engineer-on-call during business hours, paged after-hours
Why production AI decays without continuous operations.
Four failure modes that quietly erode the leverage you just paid to build.
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.
Models keep moving
Anthropic, OpenAI, and Google ship new models every few months. Someone has to benchmark, migrate, and re-eval — or you fall behind on cost and quality.
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.
Nobody owns it internally
You don't want to hire an AI platform team. But without one, the system you shipped becomes a tax on whoever is closest when something breaks.
What a Continuous AI Operations retainer delivers
We run the AI systems we shipped — monitoring, tuning, upgrading, and responding when things break — so the leverage compounds instead of decaying.
- 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
- Quarterly review with the business owner: what shipped, what to scope next
Scope before we sell you anything.
Free first steps before you commit.
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, quarterly in-person reviews. We tune, upgrade, and respond while you focus on the business.
AI leverage that compounds instead of decays.
What a retainer catches that an internal team misses.
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.
Which system are you most worried about silently degrading? Let's scope a retainer.
Talk about a retainerCommon questions
What's covered by the retainer?
Monitoring, evaluations, prompt and context tuning, model upgrades, incident response, and quarterly 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 is built on open standards (LLM APIs, vector stores, common orchestration layers) and the code is in a reasonable state, we'll scope an onboarding phase first.
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?
Monthly retainer, priced by scope — number of agents or workflows, SLO tier, and after-hours coverage. Typical starting point is a fixed monthly fee with a 3-month minimum. No per-request or per-token charges.
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)
Which system are you most worried about silently degrading?
30 minutes with a CTO. Bring the systems you already have in production (or are about to) and what running well would look like.