By Andrew Diaz · APR 28, 2026 · 4 min read
The industry’s default is to run a pilot and see what happens. We do it the other way around: we size the return before we write a line of production code.
Pilots feel like progress, but a pilot with no thesis is just spending to find out whether you should have spent. Before we build, we model the use case — the volume, the cost per resolution today, the projected cost after — so there is a number to beat and a reason to proceed.
We look at where the money actually is: the conversations that drive revenue, the volume that drives cost, the escalations that drain a team. We quantify the opportunity, model the projected cost and payback, and prioritize the use cases that clear the bar. You see the full picture before you commit.
Once the return is clear, we build the smallest thing that captures it, govern it from day one, and measure against the number we set. If it does not move the metric, we do not expand it.
AI that works starts with knowing what "works" means — in dollars, before the build.