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NLPLLMLLM-judge
Sales-call analytics (NLP + LLM)
We turn your sales team’s call recordings into clear metrics: how the conversation ended, how closely the manager followed the script, and how well they handled objections. You see not isolated calls but a live picture of the team — and the points where deals are lost.
Client: A B2B supplier with an active cold-sales team. Managers work from a predefined script with mandatory questions and commercial offers to the client.
Introduction
Call recordings usually sit as dead weight: re-listening to hundreds of conversations by hand is impossible, so the sales team gets judged by feel. We make these recordings a working tool — breaking each call down into facts and assembling a dashboard you can read in a minute.
Pain → Request
The head of sales saw final results but not what happened inside the calls: were managers asking the right questions, reaching the offer, reacting to rejections? Managers reported “all fine,” and the only way to check was manually. The ask was simple — make the team’s work transparent without manual listening.
TranscriptMetricsScript
✓ 00:42 — price quoted
✓ 01:05 — objection handled
… 01:40 — meeting booked
The project
The client handed us their conversation algorithm; we listened to the calls and for each one checked what was said against what the script requires. The output is a dashboard of three metrics: call outcome with a funnel and CRM reconciliation, script adherence at each step, and objection handling — which ones arose and how the manager closed them.
Call recording
→Transcription
→Script scoring
→Report
Impact
It became visible exactly where the team deviates from the standard: which questions are skipped, which offers never reach the client, which objections deals fall apart on. Discrepancies between the real call outcome and the CRM records surface.
Conclusion
We don’t count abstract scores — we show by the facts where money is lost in calls and why. It’s not a one-off report but a repeatable check you can run regularly and watch how handling quality changes over time.
100%
of calls analyzed
not 5%
vs a manual sample
Tech stack
NLPLLMLLM-judge