The accuracy number, and how we prove it.
94%+ classification accuracy, calibrated against human reviewers and written into the master agreement. Here is the number, how it is measured, and the clause that stands behind it.
We publish the number, and stand behind it.
Most platforms answer the accuracy question with coverage and an invitation to go check the work yourself. That is not the same as telling you how accurate the scoring is, and standing behind it.
How accuracy usually gets answered
- ~"See and verify everything yourself." Coverage stands in for a measured number.
- ~A relative claim, more accurate than a general model, with no absolute figure you can hold.
- ~A scale number, proven on billions of interactions, that says nothing about how often it is right.
- ~No published accuracy, and nothing about accuracy in the contract.
How QEval answers it
- +A published number: 94%+ classification accuracy, stated as a service level.
- +Measured against human reviewers, with the drift between them kept under 2%.
- +Every score pinned to the moment in the conversation that earned it.
- +The number is written into the master agreement, before you commit.
Left column describes patterns observed across competitor sites in June 2026. Market context, not QEval results.
Pick a call. Trace every score to the turn that earned it.
QEval does not hand you a grade and ask for trust. Click any score to see the exact moment it came from, then check the model against a human reviewer on the same call.
across four dimensions
Accuracy is a measurement, run continuously.
The 94%+ figure is not a launch benchmark that ages out. Here is what stands behind it.
Industry average automated-QA accuracy sits around 65 to 70 percent. Market context, not QEval results.
What 94% means in practice.
A percentage is easy to print and hard to trust. Here is what ours actually refers to, and where the gap goes.
The number is in the agreement.
Accuracy that lives only in a sales deck is an adjective. Ours is a service level in the agreement you sign.
Accuracy is the floor the other five layers stand on.
If the Layer 1 score is not accurate, every layer above it inherits the error. That is why the accuracy SLA is the precondition for the 82 percent of value that comes from Layers 2 through 6, not a detail.
Accuracy, in plain terms.
Is the 94% accuracy actually in the contract?
Yes. The 94%+ classification accuracy is written into the master agreement as a service level, alongside the 30-day deployment guarantee and the 60-day exit clause. You see the language before you commit.
Accuracy of what, exactly?
Of the classifications QEval makes when it scores a conversation: whether a disclosure was present, whether the issue was resolved, the sentiment of a turn, and so on. Compliance checks run higher, at 98%+ accuracy with 95%+ recall on the violations that are present.
How do you measure it?
Against people. A continuous sample of conversations is scored by trained human reviewers, and QEval's scores are compared to theirs. The drift between the model and the human is kept under 2%. When the two diverge, the model is recalibrated.
How is this different from a vendor saying "we are 95% accurate"?
A slide claim is not published, not measured against a stated method, not tied to evidence, and not in the contract. The QEval number is all four: published here, calibrated against humans, pinned to the turn that earned each score, and written into the agreement.
What if accuracy slips after we go live?
Accuracy is monitored continuously, not certified once. Scores are recalibrated against the human sample on an ongoing basis, and the service level holds for the life of the agreement, not just at launch.
What counts as a miss?
A miss is when QEval's classification on an item differs from a trained human reviewer's on the same item. It is measured on a continuous sample, not a one-time launch test, so the number reflects how the model performs on your conversations over time.
How often do you recalibrate?
Continuously. The human-reviewed sample runs on an ongoing basis, and when disagreement clusters around a pattern, the model is retuned. That is why the service level holds for the life of the agreement, not just at launch.
See the number on your own calls.
Start a pilot and we will score your conversations against your scorecards, then show you each score next to a human reviewer. The 94%+ SLA is in the agreement before you commit.