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After an AI Development build
Most clients move into Operations the month their system goes live, whether we built it or another team did. We assess what's deployed, learn how it should work, and take over the engineering ownership.
AI Operations
We keep your AI working.
Sharper than the day it shipped.
Your AI systems and agents stay sharp in production. We run them, monitor them, and improve them every month. Behavior evals, context upkeep, guardrails and audit trails, model upgrades. The discipline that makes AI compound in value instead of decay.
What's included
Seven things.
Every month.
- 01
Evals on real behavior
Trajectories and outcomes, not just outputs. Running continuously, so we see degradation before your users do.
- 02
Incident response under SLA
When something breaks, we're the first call. Not your engineering team's pager.
- 03
Context kept current
The knowledge, tools, and instructions your agents run on, kept in sync with how the business works today. Versioned and reviewed.
- 04
Cost and vendor governance
Per-use-case spend tracking. Budget caps and alerts before invoices surprise. Model and vendor decisions logged for audit.
- 05
Guardrails and audit trails
What your agents may do on their own, what escalates to a human, every action logged. EU AI Act obligations tracked as they take effect.
- 06
Model upgrades without rebuilds
New models ship every month. We re-test, swap what wins, and your system gets sharper with no rebuild.
- 07
Continuous improvement
We ship new capabilities, not just keep the old ones running.
We sell the engineering discipline that makes AI systems compound in value instead of decay.
Who this is for
AI Operations works in three situations.
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When AI runs, but no one's watching
You shipped something months ago. Nobody knows if it's still performing the way it did at launch. Drift, broken integrations, quiet degradation. We do an initial assessment, then take over operations.
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When you want better engineering
Your AI runs. Your team maintains it. But monitoring is ad hoc, evals are inconsistent, context goes stale, and new models ship without anyone re-testing. You don't need rescue. You need engineering discipline at production-grade. We bring it.
If your AI doesn't yet run in production, you're looking for AI Development, not Operations.
FAQ
Frequently asked questions
How do you take over an AI system you didn't build?
We start with an initial assessment to learn what's deployed and where the risks are. Then we take over engineering ownership: monitoring, evals, runbook, governance. Most clients see meaningful improvements in the first month.
How does pricing work?
Monthly retainer scoped to your system complexity and response SLAs. After a 30-minute call we send a clear proposal. No usage-based surprises.
How do you keep AI costs under control?
Per-use-case spend tracking, budget caps, alerts before invoices surprise. Vendor and model decisions logged for audit. We watch cost-per-outcome, not just cost-per-call.
Can our existing engineering team stay in the loop?
Yes. AI Operations works alongside your team, not instead of them. We bring the discipline (evals, monitoring, cost governance, model risk reviews) so your team can ship the next thing instead of firefighting the last.
Talk to us about your AI in production.
Walk us through what you have deployed, and we will tell you where we would take over and what we would leave alone.
Talk to a founder