For product managers shipping AI features
Unexpected compute costs. Hallucination you only discover in production. A model choice you cannot defend to your CEO.
SuperHumanly maps your feature step by step — showing where AI is actually needed, what it will cost at scale, and what failure looks like as a costed scenario.
Most steps in your feature do not need AI. The ones that do compound fast at scale. Nobody maps this before building.
Feature flow shows exactly where the cost lives
A 2% error rate sounds small. At 80 calls per day that is 57 wrong outputs per month with a real dollar cost attached.
Costed failure mode quantifies it before you build
Claude Opus costs 20× more than Haiku. For classification tasks they perform nearly identically. Most PMs do not know this.
Model recommendation with explicit reasoning
Which steps are free. Which steps cost money.
At 2% on 80 calls/day — $384/month in wrong orders. That is 27% of the feature's projected value.
What goes wrong, and what it costs.
Classification, not reasoning. Haiku matches Opus on this task at 1/20 the cost.
What this feature makes possible next.
Built by a product manager who got the cost estimate wrong, discovered hallucination in production, and wished this existed before they built it.