We built a voice analytics platform that listens to sales and support calls at scale, turning raw audio into actionable coaching signals. Every conversation is transcribed, analyzed, and summarized so leaders can focus on high-impact calls instead of sampling a tiny fraction of the queue.
Using diarization, ASR, sentiment, and intent tagging, the system detects objections, risk phrases, compliance issues, and coaching moments. Recommendations are pushed directly into the CRM timeline, where reps already work.
Quality teams get full coverage on conversations, while managers and reps receive focused insights: which calls to listen to, what went well, and where to improve.
Traditional QA and coaching had clear limitations:
We introduced an AI-first voice analytics layer:
The Voice Analytics Coach delivered:
The platform ingests call recordings from the telephony stack, runs analysis in a Python + GCP pipeline, and pushes results back into the CRM and QA tools that teams already use.
Every call gets transcribed and scored automatically, so QA and managers are no longer blind to the “middle of the pack”.
Instead of generic feedback, reps see concrete examples – timestamps where they interrupted, missed a buying signal, or handled an objection well.
The system highlights calls with aggressive sentiment, cancellation language, or missing required disclosures, helping compliance and retention teams react quickly.
Aggregated reports show which talk tracks correlate with higher CSAT, better NPS, and stronger win rates across teams and segments.
The architecture is built to extend: new languages, new sentiment models, or new CRM fields can be added without changing the core ingestion and scoring pipeline.
