AIProduct
2026-03-18 · 7 min read
Most AI initiatives stall because nobody defined what good looks like in the workflow that pays for the feature.
We treat evaluation, feedback capture, and failure modes as product requirements — not afterthoughts.
Trust compounds when operators can see why a system suggested something and how to correct it.
Ground models in your data with clear provenance. Black-box suggestions in high-stakes workflows erode adoption faster than no AI at all.
Start with augmentation, not automation — let users verify before you remove the human checkpoint.