Delivery model
Each stage ends with an explicit decision, owner and evidence. The work can stop with a smaller process change or no build.
We map the current workflow, its boundaries, exceptions, human decisions and evidence gaps before recommending automation.
We define the AI task, allowed context, expected output, evaluation examples and the conditions that route work to a person before choosing a model. We name what AI may do, what a person approves and where exceptions go.
Connect the required tools. Bound the AI steps. Add approvals, logs, error handling and recovery paths. Leave with an evaluation-ready workflow and explicit operating limits.
We evaluate agreed examples, exceptions, failure paths and human decisions, then record gaps instead of hiding them.
The delivery method includes evaluation criteria, logs, error handling, recovery steps, a playbook, a named owner and a review cadence.
Product Engineering Support covers only the web or mobile interface required by an approved workflow.
Bring one workflow and enough context to decide whether discovery is useful.