How AI auditing scores call quality against your own benchmarks
CallKaro AI audits a random sample of calls against parameters you define, scoring quality and explaining the reasoning behind every result.
Manual reviews do not scale
Most quality teams can review only a small number of calls by hand a handful per agent each week, if that. Whatever gets picked this way is not a fair sample, and two reviewers can score the same call differently depending on what they notice.
CallKaro AI's auditor removes that ceiling. It pulls a random sample of calls from a campaign or agent and reviews every one of them against the same set of parameters, every time.
You define what a good call looks like
The auditor works off benchmarking parameters you set things like whether a required disclosure was read, how the agent handled an objection, whether the resolution matches what was promised, or how the call was closed. These are not generic scorecards; they are built around your process.
Each call is checked parameter by parameter, and every result is tied back to what was actually said in the transcript. A low score on "objection handling," for example, points to the exact moment the objection was missed a clear area of improvement instead of a vague note.
Every score comes with a reason
An AI reviewing calls can sound like a black box unless you can see why it reached a conclusion. CallKaro AI's auditor attaches a rationale to each parameter score, so a supervisor can check the reasoning against the transcript before acting on it.
The point of the audit is not to catch an agent doing something wrong. It is to show, call after call, where the process is breaking down and what a better call would have looked like instead.
