Research
AI research and evaluation: measurement, not opinion.
Every recommendation we make is backed by evals built from a client’s own data. This is where we publish the methods behind that work — how we build eval sets, what a “build” verdict actually requires, and what we’ve learned about the real cost of running these systems once the demo is over.
A payback figure is only as good as the test set under it.
So we show you the whole test set: where the questions came from, who graded them, and exactly what we compared against. You can check every number we give you.
How we measure
We pull real questions from your ticket queue and your internal channels, grade them once by hand, then score your existing tooling and the AI approach the same way. The gap between those two scores is what your budget buys.
What we’ve found
The pattern that repeats: simple tooling scores well, and AI adds a real gap on top of it. Sometimes that gap pays for itself comfortably. Knowing which case you are in, in your own numbers, is the whole job.