AI Ops
Production AI degrades without maintenance. Monitoring, evals, prompt and model updates, cost control — monthly.
- Format
- RETAINER — MONTHLY REPORT
- Price
- Monthly, in advance.
- Terms
- Quarterly agreements
- Ladder
- Rung 04 of 05
00 — Best for
- You run AI in production and quality is drifting.
- Model costs are climbing and nobody owns the bill.
- Providers keep changing models under your system.
01 — Why AI systems need ops
An AI system is not done when it ships. Models get deprecated, providers change behavior between versions, prompts that worked in March quietly fail in June, and costs creep while nobody watches. Most AI failures we see in the wild are not build failures — they are maintenance failures.
AI Ops is the standing discipline that keeps a production AI system honest: evals that measure quality continuously, monitoring that catches drift early, and a monthly report that tells you in plain language what the system did, what it cost, and what changes next.
02 — What you get
- Monitoring and alerting on quality, latency and cost
- Eval suites that catch regressions before users do
- Prompt and model updates as providers change underneath you
- Cost optimization with a monthly spend report
- A written monthly report: quality, incidents, spend, next moves
03 — Straight answers
Do you maintain AI systems built by others?
Yes — it usually starts with a short paid audit and an eval baseline, so improvement is measured against something real.
Who pays the model bills?
You do, on your own provider accounts — we optimize the spend and report it monthly, but the accounts and the data stay yours.
Sound like your next step?
Start a project