Watch the human, not the model.
The engine is the instrument for the user side of AI. It reads every conversation your deployment has and measures what your model is doing to the humans on the other end, turn by turn.
We ran the engine on a published dataset across nine deployment archetypes and opened all of it: the findings, the methodology, and the generator that produced the data. Every median is checkable from the source it points at.
Eval tools measure the model. BIE measures the user.
Your evals measure the model. Your CX dashboards count tickets. We measure what your AI is doing to the people on the other end. A different layer than your stack, not a replacement.
Five products that passed every test.
Every dashboard reported healthy. The user-side reality diverged.
A headline you can't dismiss.
The claim, the observation that would falsify it, and the one thing to ship this week. It reads like a senior analyst wrote it, because the engine is built to argue a case and defend it.
If nothing changes, this flow loses measurable CSAT before the week is out, and you will see the first sign well before then.
The full version lands in your inbox each week. View a complete sample report →
Refund flow
Trust came apart partway through the duplicate-charge flow this week. The bot doubled down with confident misinformation, and most of those conversations ended without a fix and resurfaced as tickets days later.
| Trust calibration | down sharply ▼ |
| Frustration buildup | escalating, compounding across turns ▲ |
| Silent abandonment | 8.2% ▲ |
Reroute refund queries past turn 3 to human escalation until the prompt is corrected.