How QEval keeps its scoring fair and accountable.
Fairness in automated scoring is something you can measure, not just promise. Here is how QEval keeps scores accurate, explainable, consistent, and private, and what we deliberately do not do.
Fairness you can measure.
An automated score is only fair if it is accurate, explainable, consistent across every conversation, and built on data you control. Those are testable properties, not promises.
Four ways we keep scoring honest.
Pick one. Each is a mechanism you can check, not a value statement.
One scorecard, applied the same way every time
A score should not depend on which reviewer looked, what day it was, or whether the agent was a person or an AI. QEval applies one scorecard and one set of rules to every conversation.
Scores are calibrated against people, and watched for drift
QEval scores are checked against trained human reviewers on a continuous sample. The gap between the model and the human is held under 2%. When it drifts past that, the model is recalibrated.
Every score points to the moment that earned it
No black-box grades. Each score links to the exact turn in the conversation behind it, so an agent, a supervisor, or an auditor can check it in seconds.
Your conversations stay yours
Personal information is redacted at ingest, before any model sees it. Scoring runs on QEval's own closed mixture of experts, so your conversations never enter a third-party model's training loop.
What QEval does not do.
The clearest way to describe responsible scoring is to be plain about the lines we do not cross.
The same standard, held across everyone.
Bias in scoring usually creeps in through inconsistency: who reviewed, what mood, which day. A model removes that variance only if it is watched for new variance of its own.
Part of a wider trust posture.
Responsible AI, in plain terms.
Do you train AI models on our conversations?
No. Scoring runs on QEval's own closed mixture of experts, and your conversations never enter a third-party foundation model's training loop. Personal information is redacted at ingest, before any model processes the conversation.
How do you keep scoring from being biased or inconsistent?
One scorecard and one set of rules are applied to every conversation and every agent, human or AI. Scores are calibrated against trained human reviewers, so the result does not depend on reviewer mood or which 2% of calls happened to be sampled.
Can we see why a score was given?
Yes. Every score is pinned to the exact moment in the conversation that earned it, so an agent or an auditor can verify it directly rather than trust a number.
What standard do you hold yourselves to?
QEval is built to ISO 42001 for AI management, alongside SOC 2 Type II, ISO 27001, PCI DSS, HIPAA, and GDPR. Accuracy is held to the contractual 94%+ service level.
Does QEval replace human reviewers?
No. People define the scorecard, calibrate the model against their own judgment, and handle the genuinely ambiguous cases. QEval does the volume, scoring every conversation, so your reviewers spend their time on the hard calls instead of sampling.
See it run on your conversations.
Start a pilot and we will score a sample of your conversations, show you each score pinned to its evidence, and walk your security and quality teams through how it is calibrated and protected.