Call monitoring

Call barging happens when a supervisor joins a live customer call as an active participant. Both the agent and the customer can hear the supervisor once they join. This differs from listen-only monitoring, where the supervisor stays silent, and from whisper coaching, where only the agent hears the supervisor. In AI-assisted centers, barging can also mean a human stepping into a call that an AI voice agent is handling. For example, a supervisor sees a compliance phrase flagged in real time and joins the call to resolve the issue directly with the customer. Because barging grants access to a live conversation, permissions and logging matter. Teams should record who can barge, when, and why. OnClarity’s AI Agent QA can export QA scoring as part of a full audit trail, which matters for regulated contact centers that need to show reviewers exactly who touched a given call.

Quality assurance is the structured review of conversations against a scorecard, applied to human agents and AI agents alike. Traditional QA samples a handful of conversations per agent each month; automated QA scores every conversation, which changes the exercise from spot-checking into actual measurement.

QA coverage is the percentage of customer service conversations reviewed and scored against a quality rubric. It measures how much of an operation’s actual customer interaction volume actually gets evaluated. Manual QA programs typically sample only a small slice of contacts. Industry data puts the standard at 2 to 5 calls per agent per month, often a low single-digit percentage of total volume. Everything outside that sample is a blind spot, where compliance breaches, coaching opportunities, and recurring issues go undetected. OnClarity’s AI Quality Agent scores 100% of conversations within about five minutes of call end, roughly 20 times typical manual coverage. That shift turns QA from a sampling problem into a question of rubric consistency and audit-trail completeness.

PII redaction is the automatic removal of personally identifiable information from transcripts, logs, and training samples. It lets support conversations be analysed, stored, and used to improve models without carrying customer identifiers into systems that were never scoped to hold them.

Speech analytics automates analysis of recorded or live customer calls. It uses speech-to-text transcription combined with natural language processing to extract keywords, topics, and sentiment. Many systems also measure acoustic signals such as silence, talk-over, and speaking pace, to flag stress or disengagement that word choice alone would miss. Real-time speech analytics prompts agents mid-call and raises live compliance flags. Post-call analysis surfaces trends and root causes across a larger call sample. Standard uses include detecting required compliance phrases, categorizing contact reasons, and feeding evidence into quality assurance scoring.