Retainers
Not only after launch. From the first real user, we monitor what your AI and systems do, alert you when something goes wrong, and send reports you can read in five minutes. Fixes are included, and the extra tooling is there when you need it.
What you get
Why it needs a person
A Routine Run routine
Where we start on day one of a retainer. Run it yourself first: if any step makes you hesitate, that's the first thing to watch.
Keys out of the code
Secrets in a vault, not in prompts or the repo.
Production apart from staging
Separate projects, so a test never mixes with real traces.
Trace every step
Tools and retrievers too, not only the model call.
A golden set of 30 real cases
Run it before every prompt or model change.
Cost per run, with a limit
Know it per conversation, not from the monthly bill.
A runbook for the night it breaks
Who is alerted, what they check first, how to roll back.
AI oversight
We watch what your AI does, alert you when it goes wrong, and once a month read real runs and tell you what we found, before your customers do.
A weekly look at your AI in production, with the fixes included. The test set grows from every real failure, so a mistake is caught once and then never again.
For AI that carries part of your business. Someone who knows your system is on it every week and answers the same business day when it misbehaves.
His mindset always balances speed with stability, which makes him an invaluable contributor to production systems.
Every retainer starts with the one-week AI agent audit ($2,400), credited against the first month.
What a review looks like
Add-ons
Outcomes, quality, cost per task and what needs a person, on one screen your managers read without us.
Slack, Telegram, email or SMS, routed to the person who can act, with the trace one tap away.
An automatic check for what good means in your work, calibrated against your experts until it agrees with them.
We try to make your AI leak, misbehave or act outside its role, and hand you the fixes ranked by risk.
Where the money goes per run and per agent, and what to change: caching, a smaller model, shorter context.
Who asked what, what the AI did and who approved it, exported in a form your auditor or client can read.
Care plans
Every September Apple ships a new iOS and watchOS, and App Intents, Siri and the on-device model change with it. Your app keeps up without a scramble.
For an AI interviewer or expert system in daily use: your experts get help with the taxonomy and prompts, and quality is measured every month.
Monitoring that reaches the right person, backups that have actually been restored, dependencies and security patches kept current, and a cloud bill reviewed line by line.
An experienced engineer on your side of the table: architecture decisions, vendor and hiring checks, reviews of the work that matters.
Local businesses around Palm Coast have their own care plans, from $150 a month. See them
Why us
Check it yourself
Questions
Yes, and it is the best moment. Monitoring set up with the first real users catches the failures nobody tested for, while they are still cheap to fix.
No. We start every retainer with a one-week audit of what you run today, priced at $2,400 and credited against the first month if you continue. If there are no traces to read yet, setting them up is the first fix.
Three months at a time. After that it runs month to month and stops with 30 days notice.
They carry over one month, then expire. Work beyond the plan is quoted at a fixed price before we start it.
You do, directly or at cost. The retainer is our work, never a markup on tokens.
LangSmith, Langfuse, OpenTelemetry traces or your own logs; OpenAI, Anthropic, Google, Apple and open models; agents in LangGraph or your own code.
