Practical notes from the engineers who build agents, copilots and AI products.
Fraud models score in real time, are validated on hold-out periods and monitored for drift as fraud patterns change. The model sits behind rules and in front of analysts: rules catch the known, the model catches the unusual, analysts label what they review, and those labels retrain the model on a schedule.
Fraud models score transactions in real time, validated on hold-out periods, with precision tuned to the cost of a false positive. The hard parts are labels that arrive weeks late, fraud patterns that shift as soon as you block them, and a review team that has to trust the score enough to act on it.
Fixed price for scoped programs, time and materials for open-ended research; the mistake is fixed price without a scope lock. AI adds a twist: accuracy cannot be promised before it is measured, so the contract should fix the scope, the date and the evaluation method, and let the discovery or POC sprint set the target.
Fixed price works because scope is locked early, change goes to a priced backlog and hardening has its own week. Every Eazyware program has a fixed fee, a fixed calendar and an evaluation threshold that defines done, and the method behind that is a set of unglamorous habits rather than heroics near the deadline.
Fixed price works when scope is locked early and change goes to a priced backlog; it fits discovery, POCs and MVPs best. The buyer gets a date and a number they can put in front of a board; the builder takes on the delivery risk and manages it with short programs, evals and a strict change process.
AI discovery sprint projects fail for five repeatable reasons: no decision is attached to the output, the operational owner is missing, data is described rather than opened, the sprint turns into a build, and nobody owns the result on day eleven. All five are scoping failures, and all five are preventable.
Field apps need offline routes, proof of delivery, payments, navigation and a fast sync; anything else is secondary. Technicians judge an app on whether it works in a basement, captures a signature in one tap and never loses a job, so design the five core flows first and keep dashboards and chat for later.
Flags let you ship to a beta cohort, measure and roll back without a release; every AI feature should launch behind one. The discipline is separating deploy from release, choosing cohorts deliberately, deciding the metric before the flag turns on, and deleting the flag when the rollout is done.
Exception agents read events, message the customer, reschedule within rules and escalate damage or refusal with context. Delivery exception management is the highest-return agent in last-mile logistics: each exception costs a rider's wait, a hub call and a customer's patience, and most follow a writable rule.