The Scale of Untapped Audio Data
A large contact center handles tens of thousands of calls per day. Each is recorded for regulatory reasons or training purposes — and is virtually never listened to. Resource constraints mean that even with an intensive QA program, only 1-3 percent of calls are manually reviewed, selected randomly or based on simple filters. The rest disappears into archives, taking with it information about recurring customer problems, agent procedural errors, potential compliance violations, and churn signals.
Transcription as the Foundation of Analysis
Automatic transcription converts the audio stream into searchable text, opening the possibility of analyzing 100 percent of calls. Transcription quality is critically important for all subsequent applications — errors in recognizing key words can both generate false compliance alarms and cause real violations to be missed. Transcription models specialized for industry language — with a vocabulary of products, processes, and typical queries — achieve significantly higher accuracy than generic models. A particular challenge is diarization — distinguishing speakers, assigning utterances to the agent or customer, and handling speaker interruptions.
Sentiment Analysis and Emotion Detection
Beyond conversation content, the way it is conducted carries important context. Utterance-level sentiment analysis tracks the evolution of the customer's mood throughout the call — escalation of frustration, the breakthrough moment after problem resolution, signs of resignation. Acoustic emotion detection — analyzing speech tempo, tone, pauses, and interruptions — provides additional signals absent from the text alone.
- Emotional escalation indicator — rises when the customer repeats the problem or raises their voice
- Agent silence — an indicator of uncertainty or lack of knowledge about the solution
- Agent speech tempo — too fast may indicate time pressure, too slow may suggest difficulty with systems
- Interrupting the customer — correlates with negative satisfaction scores
- Positive emotions at call end — a predictor of loyalty and likelihood of return
Automated Service Quality Assessment
The traditional QA scorecard filled out by a supervisor after listening to a call is replaced by automatic scoring of every conversation. The system verifies script adherence, use of prohibited phrases, completeness of mandatory disclosures, problem resolution time, and resolution effectiveness (whether the customer called again about the same issue). Supervisors receive an automatically curated review of calls requiring attention — not a random sample but actual cases deviating from quality standards.
Compliance and Regulatory Monitoring
In regulated sectors — finance, insurance, telecommunications, healthcare — call recordings are subject to retention and availability requirements for regulators. Speech analytics systems automatically verify compliance with disclosure requirements — whether the customer received required notices, whether all terms of the offered product were presented, whether consent for data processing was expressly given. Near-real-time alerts allow supervisor intervention within the same day, rather than after a week of archival processing.
ESKOM.AI builds speech analytics systems integrated with existing contact center infrastructure, delivering not just transcription and scoring but a complete picture of customer experiences, operational quality, and compliance status — at a scale impossible to achieve through traditional methods.