AI quality assurance
Every other function in your company is measured on all of its output. Support gets measured on a sample somebody picked by hand. Here is what that costs you, and what changes when the sample is all of it.
Take a mistake that happens in one of every 200 conversations. At 50,000 conversations that is 250 times a month. At 1.2% coverage you would expect to see it three times. Three instances reads as bad luck, and nothing in your data would tell you otherwise.
The sample is not random either. Reviewers pick conversations that are quick to review, which pulls it toward short tickets and away from the long messy ones where things tend to go wrong.
Every QA lead asks this, and it is the right question. If they do not believe the score, nobody acts on it.
Each judgment cites the passage in your SOP or knowledge base it was measured against. Your QA lead can open it and check.
Your QA leads grade a sample by hand. We tune until the AI agrees with them, and you sign off before it goes live.
An agent who thinks a score is wrong can see exactly what it was based on and raise it. Disagreements feed back into calibration.
We do not trigger warnings, rankings or discipline. Results go to your QA lead and they decide what to do.
How RevelirQA compares, side by side.
One scorecard, applied to both.
Six weeks, and your QA leads decide whether we got it right.
Helping teams deliver great Customer Service consistently