Practical notes from the engineers who build agents, copilots and AI products.
Your AI vendor is out of their depth when demos replace measurement, when nobody can show you a request trace, when every quality problem is answered with a prompt edit, and when there is no plan for a wrong action. These signs appear mid-build, not at pitch.
You reduce AI inference cost by changing one lever at a time behind an eval gate: route easy work to a smaller model, cache the stable part of the prompt, cut context you never needed, and batch what nobody is waiting for. Measure accuracy before and after. Keep only what holds.
Scope an AI agent to ship in six weeks by fixing one intent, no more than three tools, a frozen evaluation set and a written cut list before week one. The date holds because the scope is decided in advance, not because the team works faster than physics allows.
Compare AI proposals by normalising them before you compare totals: same deliverable list, same acceptance criteria, same twelve-month running cost, same ownership terms. An hourly rate was never the useful number anyway, because it prices effort rather than the outcome you are buying.
AI maintenance support is not bug fixing. It is a recurring calendar: weekly evaluation runs, prompt regression on every change, re-indexing as content moves, model deprecation migrations, cost monitoring and a named response time. Eazyware Care Plans start at $1,000 or ₹68,000 a month.
Moving AI from pilot to production changes six things: who owns it, how it is measured, what it is allowed to do, how fast it must respond, what it costs every month, and who answers at 2am. The model is usually the only part that stays the same.