A sales department recorded every call and reviewed almost none of them. We built an agent that transcribes each conversation, scores it against the team's own quality checklist, mines recurring customer objections and delivers the result into Telegram. Coverage went from an unmeasured sample to 100% of calls, and the head of sales recovered more than half the time previously spent listening.
A sales department recorded every customer call, and almost nobody listened to them. The head of sales could realistically review a few conversations a week out of hundreds, which meant coaching was based on whichever calls happened to be sampled and whichever deals went loudly wrong. Nobody could answer basic questions with evidence: which objections come up most often, which script steps get skipped under pressure, whether a new hire is actually improving. Historical recordings were worse — a growing archive that everyone agreed was valuable and nobody had the hours to mine. The department needed objective, repeatable evaluation of every conversation, delivered somewhere managers already look, without adding a headcount whose entire job is listening to audio.
Calls are ingested and transcribed automatically through an ASR service, with speaker separation so customer turns and manager turns can be scored independently. Transcription runs continuously, so a call is analysable minutes after it ends.
Each transcript is evaluated against the department's own quality checklist — greeting, needs discovery, objection handling, next-step agreement. Scoring is applied identically to every call, which removes the sampling bias that made previous quality reviews arguable.
Across calls, the agent clusters recurring customer questions and objections, surfacing what the market actually pushes back on. This turns individual call reviews into an input for scripts, pricing conversations and product messaging.
Summaries, per-manager scores and flagged conversations are pushed into a Telegram bot where the sales leadership already works. There is no separate dashboard to remember to open — the analysis arrives where decisions are made.
A backfill mode reprocesses the existing archive, so the system launched with trend data instead of starting from an empty baseline. Managers could compare current performance against months of prior calls from day one.
Coverage moved from an unmeasured sample to 100% of calls, and the head of sales recovered more than half the time previously spent on manual listening — time that now goes into coaching the specific conversations the system flags. Division heads get the same view for their own teams, so quality discussions start from a shared record rather than from competing impressions. The recurring-objection reports have become an input well beyond the sales floor, since they describe what customers actually resist in their own words. The system is in active daily use and continues to be developed.
Tell us what the process looks like today and we will tell you what can be automated — and what should not be.
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