Not every workflow needs a model. Before we scope AI for a client, we ask whether the problem is actually about language, judgment, or unstructured data — or whether rules, integrations, and a clearer UX would solve it faster.
Questions we ask first
Is there a measurable cost to manual work today? Can errors be tolerated during a pilot? Do you have examples to evaluate against? Is a human still required in the loop for compliance or quality?
If the answer to most of these is vague, we usually recommend starting with automation around existing systems — webhooks, scheduled jobs, better admin tools — and revisiting AI once the process is stable.
When it is worth building
AI tends to earn its place when volume is high, inputs are messy (email, PDFs, tickets), and the downside of a wrong draft is low because someone reviews before anything goes live. That's the bar we use before proposing production AI.
