Automation moves information or triggers actions according to known rules. AI is useful when the input is less structured — for example, extracting meaning from a document, classifying a request, summarizing a history, or drafting a response. Reliable systems often combine both: deterministic rules control the workflow while AI handles a narrow judgment task inside explicit boundaries.
We begin with the work rather than the model. A process that is unclear, inconsistent, or rarely repeated is usually a poor automation candidate. We look for stable, high-volume steps where people retype data, chase status, assemble the same document, or make a predictable first-pass decision. The value should be observable in saved effort, shorter turnaround, fewer errors, or better service.
Production AI needs more than a prompt. Sensitive data must be handled deliberately, outputs need validation, failures need a safe path, and people need to know when they are reviewing machine-generated work. We design those controls with the workflow, then monitor quality after launch instead of assuming a prototype will behave perfectly forever.
