AI Ops & Reliability Retainer
Once an AI agent or automation is live, something has to keep watching it. This monthly retainer covers monitoring, evaluation against a real baseline, and incident response — for systems we built or hardened, or ones you already have.
- Ongoing monitoring of output quality, cost, and failure behavior
- Evaluation against a real baseline — catches drift, not just downtime
- A named team responsible for incident response, not a dashboard
- Natural next step after a Production Readiness audit and sprint
Service Focus
Delivery Ready
$2k – $8k
Monthly Retainer
Monthly
Reliability Report
Cancel Anytime
No Lock-In Contract
Any AI System
Ours or Yours
AI Systems Don't Fail Loudly.Someone Has to Be Watching.
A crashed server tells you immediately. An AI agent quietly giving wrong answers, drifting off-brand, or burning through API budget doesn't — it just keeps running until a customer notices first.
This is a monthly retainer, not a one-time build: ongoing monitoring, evaluation against a real baseline, and a named team accountable for catching problems before they become customer-facing.
What's Actually Watched
- Output Quality vs. Baseline: Whether responses are still accurate, on-brand, and useful — not just online.
- Cost & Usage: API spend and usage patterns, so a spike gets caught before the invoice does.
- Failure & Fallback Behavior: What happens when the system doesn't know the answer — and whether that's handled gracefully.
- Model & Vendor Changes: Underlying model updates that can silently change behavior without any code changing.
Who This Is For
- Post-Production Readiness Clients: The natural next step once an audited, hardened system is live.
- Teams With Their Own AI Agent: Already built or bought one, and nobody is formally responsible for watching it.
- Companies Past a Reliability Incident: Something already went wrong once, and it can't be allowed to happen quietly again.
Why a Retainer, Not a Dashboard License
01
Judgment, Not Just Alerts
A monitoring tool flags anomalies. A person decides which ones actually matter.
02
Same Team, Full Context
Whoever hardened or built the system is who's watching it — not a handoff to a support queue.
03
Cancel Anytime
Monthly, not a locked-in annual contract — the retainer has to keep earning it.
04
Feeds Back Into the Build
What we see in monitoring shapes real fixes, not just a report nobody reads.
What the Retainer Covers
The unglamorous, ongoing work that keeps an AI system trustworthy after launch day — not a one-time setup.
Ongoing Monitoring
We watch how your AI agent or automation actually performs in production, not just whether the server is up.
Incident Response
When something goes wrong — a bad response, a broken integration, a cost spike — someone is already looking, not waiting for a customer complaint to find out.
Evaluation & Drift Tracking
Model and prompt behavior shifts over time and with vendor updates. We track output quality against a baseline so degradation gets caught early.
Prompt & Model Updates
As underlying models change or new ones become worth switching to, we handle the update and re-validation instead of leaving it to drift.
Monthly Reliability Report
A short, honest report on what happened, what we changed, and what's worth watching next — not a vanity dashboard.
A Named Owner, Not a Dashboard
An automated monitor can't decide whether an anomaly matters. This retainer means an actual person is accountable for that judgment call.
How the Retainer Runs
A baseline, ongoing monitoring, a monthly review, and incident response when it's actually needed.
Baseline & Instrumentation
We establish what 'working correctly' looks like for your specific agent or automation and put the monitoring in place to detect deviation from it.
Ongoing Monitoring
Continuous tracking of reliability, cost, and output quality — not a one-time setup that's forgotten after month one.
Monthly Review
A short report and a working session on what changed, what needs attention, and what's worth improving next.
Incident Response, As Needed
When something breaks, you're not troubleshooting alone or waiting on a ticket queue — the same team that built or hardened the system responds.
From the Blog
Practical guides on evaluating, monitoring, and maintaining AI features once they're live in production.
Already Live? Let's Talk About What's Watching It.
If an AI agent or automation is already running your business and nobody's formally responsible for its reliability, that's worth a conversation before it's worth an incident.