AI intelligence

AI quality control for sales calls

Team leads managed to hear 2–7% of sales calls, and no two reviewers scored alike — an AI evaluation layer now reviews 30–40% of the sales calls that matter, on one rubric, at about $0.30 a sales call.

Built for

E-learning — phone-first sales, Poland

Call transcriptionLLM scoringTeam reports

30–40%

of sales calls reviewed, up from 2–7% — same pipeline scales to ~100%

~$0.30

to transcribe and score one sales call — a QA specialist costs multiples and doesn't scale without hiring

One rubric

comments + per-criterion scores, identical for every sales call — no reviewer-to-reviewer drift

Built for

E-learning — phone-first sales, Poland

On this page

“We were sure AI couldn't evaluate sales calls consistently — let alone better than a human. Then we built the criteria together, focused the evaluation on what really matters for our sales, and the reports became something we actually run the team on.”

— Head of Sales, e-learning provider

The company

An e-learning provider on the Polish market. Courses are sold over the phone: a floor of sales agents, team leads coaching them, and a Head of Sales answering for the numbers. Sales-call quality was controlled the traditional way — by ear, when there was time.

The problem

Depending on the week's capacity, team leads could listen to and score between 2 and 7 percent of sales calls. The other 93+ percent nobody ever heard.

And the reviews that did happen didn't agree with each other:

Different team leads scored the same sales call differently — bias, attention, effort, and different readings of the same written process.
A weak spot — sloppy validation, a mispresented program, a script deviation — could run for weeks before anyone caught it, because catching it meant hours of listening.
Feedback reached agents late and unevenly, without examples anyone could pull quickly.
The company wasn't short on will to control quality — it was short on a way to do it consistently and at scale.

Scoring sales calls is exactly the kind of judgment everyone assumes only a human can do — and exactly where humans disagree the most.

What we did

Criteria first, built with the client

Together with the client we defined what a good sales call actually is for their motion — the research, the SPIN questions, the program presentation, the script beats that matter. The evaluation system scores business reality, not generic politeness. This is also what made the client trust the scores later.

An automatic review funnel

Sales calls flow into review by filter — the conversations where the deal actually moves (say, a lead going from new toward payment): full sales dialogues, not random snippets. Each sales call is transcribed and evaluated by the AI: comments on what was strong and what was weak, plus scores per criterion.

Reports the team runs on

Scores, comments, and the exact call examples land in the team's working reports. Coverage was set at 30–40% of sales calls deliberately — plenty for solid coaching feedback while keeping AI spend in check; the same pipeline scales to ~100% whenever the business wants it.

The payoff

What changed

Review coverage went from 2–7% to 30–40% of sales calls — and scales to ~100% when needed.

One rubric instead of opinions: every sales call scored the same way, comments and points per criterion.

The Head of Sales sees who is underperforming and where — weak validation, a mispresented program, script deviations — straight in the report, with the exact sales calls attached, before listening to anything.

Repeating patterns become fixable: coach the agent on real examples, fix the script, watch next week's scores.

The economics flipped: about $0.30 to transcribe and score a sales call, against QA-specialist review that costs multiples and only scales by hiring.

The layer we built

The client was sure AI couldn't judge sales quality — consistently, or at all. What changed their mind wasn't a smarter model. It was criteria built with the business, an evaluation system tuned to what actually drives their sales, and reports the team actually uses. That's the layer we built.

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