Catch the errors neither your engineer nor your AI catches alone.
"We instrument your code-review loop with a coupling layer and measure, against a pre-registered criterion, whether it catches what your stack misses. You keep the readout either way."
What the pilot includes
- A measurement layer installed in one code-review workflow (~6-week deployment; no hardware, no biometrics). It adds a brief structured checkpoint after each AI output, so uncertainty gets surfaced instead of waved through.
- A second-look pass: flagged outputs get reviewed again, and every flag is logged against confirmed errors, so you can see the deployment working.
- 90 days of flagged-vs-confirmed correlation tracking on your real review stream.
- A per-review readout: explicitly an instrument in validation.
- Design-partner terms: roadmap input, early-adopter pricing on the productized version when it exists, and an end-of-pilot case-study conversation. An ask, not an obligation.
The pre-registered criterion, stated plainly
"Before we start, we register the success criterion: outputs your reviewer flags are wrong at ≥2x your base rate. If we hit it, that's the case study. If we don't, you keep the data and the readout, and we keep the learning. Either outcome is honest."
What we ask of you
- Active AI use in your review or codegen loop.
- One workflow to host the layer.
- A named base error rate, or week one establishes it.
- Contracted data-capture consent. (Your code stays in your tenancy by default; specifics live in the SOW.)
- The 90-day window.
Status + fit
Pilots are paid. Three to five partners this cohort. That's our real capacity. Scope and pricing are set per partner in the intake call.
Patent pending. · Research roadmap →