AI Operations

AI systems drift. Models get deprecated or quietly change behaviour, an API you depend on ships a breaking change, a supplier starts sending a different document format, and an automation that worked in March is silently wrong by June. AI operations is the work of noticing that before your customers do: monitoring and alerts, model, prompt and integration updates, evaluation checks against known cases, and cost reporting so the model bill never arrives as a surprise.

Three retainer tiers cover most businesses. Core at $1,500 a month is monitoring and alerts, model, prompt and integration updates, a monthly evaluation check and three hours of changes. Plus at $2,950 a month adds incident response, eight hours of changes and a quarterly review. Scale at $5,500 a month runs a weekly improvement cycle of up to twenty hours and delivers one new workflow automation every month. Alongside them sit AI search visibility at $2,500 to $3,500 a month, which works on being cited in AI answers rather than only ranked in Google, and the fractional AI architect at $4,500 to $9,000 a month for ten to twenty hours of embedded leadership.

Operations runs on a fixed rhythm rather than on whoever shouts loudest. Alerts are triaged the day they fire. Evaluation runs monthly on the core tier and on every change at the higher tiers. You get a report each month covering what ran, what failed, what was changed and what it cost. On the scale tier the next automation is taken from the backlog the audit or roadmap produced, so the estate keeps growing without a fresh sales conversation each time.

This suits any business running an automation or agent the business now depends on, which in practice is most businesses about four weeks after the build lands. It suits companies with an internal IT team that can keep servers up but has nobody who owns model behaviour. It is not needed if what was built is small, stable and cheap to rebuild, in which case take the handover documentation and run it yourself.