QA listens to a sample.
The AI reads all of it.

Sales quality assurance software does not have to mean hiring a team of reviewers. In Botseller every call and every chat already lands in the customer record as a transcript and a short summary, so a manager sees what was actually said, not a random sample of four recordings a month.

A human reviewer can physically listen to only a fraction of conversations, and failed deals rarely turn up in a random sample. Which is why the AI reads all of them: transcripts and summaries are live today, while deeper checklist scoring, covering sales stages, objection handling and forbidden phrasing, is rolling out in early access with pilot customers. We would rather under-promise here than sell you a score you cannot trust.

Your supervisor does not disappear either: they calibrate the criteria, review borderline cases and coach the team. Setup needs no developer, since the checklist is described in plain words. Available on the Business plan.

The need for quality control usually arrives through pain: an important customer walked away and nobody knows what was said to them. The classic answer is to hire a reviewer, or a whole department. But a human listens to recordings in real time, so in a company with forty operators an in-house QA function manages to score four or five calls per employee per month. Bonuses are calculated, and whole teams are judged, from that tiny sample.

The sample is subjective on top of that. One reviewer is stricter, another softer, attention fades by evening, and the score depends on whose ears the call landed in. So reps dispute their points, reviews turn into arguments about fairness, and hiring a second and third reviewer only multiplies the disagreements. Scaling QA with people is slow and expensive: a new reviewer arrives later than the growth in conversations.

The problem is not that people work badly, it is that the task is not built to human scale. Thirty reps generate hundreds of conversations a day, and only a machine can cover all of them. So the split is natural: the AI reads everything, while people keep the work a machine cannot do, calibrating criteria and coaching the team.

Quality control stops being a lottery. Every conversation from yesterday, call and chat alike, is transcribed and summarized by morning, with the summary sitting in the customer record. So any conclusion can be checked in a minute against the actual words, instead of a recollection retold at a meeting.

On top of that live layer, checklist scoring is being rolled out in early access: pilot customers describe their criteria and get per-conversation scores with quotes. It is deliberately gradual, because a score you cannot defend is worse than no score at all, and a criterion that has not been calibrated on your own conversations will misfire.

The role of people changes but does not vanish. A supervisor reviews the conversations the AI flagged, calibrates the checklist and runs sessions with the team. So one supervisor plus AI covers the volume that used to require a room full of reviewers, and quality stops depending on who found time to listen.