Most AI features die the same way: a polished demo, a week of curiosity, then the team goes back to the spreadsheet. The work that lasts is quieter. It sits inside a job people already do, and it makes that job shorter without asking them to become prompt engineers.
Start from a job, not a model
Before you pick a model, write the job in one sentence: who does it, how often, what they look at, and what “done” looks like. If you cannot name the input and the output, you do not have an AI feature — you have a chat box. Stacknify builds from that sentence. The model is a component. The product is the job.
- Name the operator, not a persona. Who opens this screen on a Monday.
- List the source of truth. If the data is messy, the feature will lie with confidence.
- Decide what happens when the model is wrong. A retry, a queue, or a human review — not a shrug.
Keep a human in the loop until the loop earns trust
Operators keep features they can correct. A draft they can edit beats an answer they cannot challenge. Show the source. Show confidence only when it maps to something real, like “this invoice matched three prior bills.” Hide the rest. Confidence scores that mean nothing get ignored, then the whole feature gets ignored.
The first version should be slower than the demo. Review, edit, approve. When the edit rate drops and the error rate is known, you can tighten the loop. Skipping that step is how teams ship a bot that nobody wants to defend.
Measure week two, not launch week
Launch week is a novelty. Week two is the truth. Track whether the original job still happens in the old place. If people paste the same prompt every morning, turn that prompt into a button. If they export to Excel to finish the work, the feature is not done. Keep the ones that cut steps. Cut the ones that add a new tab.
