Quality and compliance, LLM-graded on 100% of interactions.

An LLM grades each interaction against your eval rubric, yes/no, multiple-choice and range criteria with weightings, and returns a scorecard in about 5 seconds. Because it runs continuously rather than relying on a reviewer's time, coverage goes from a ~2% manual sample to 100% of voice, chat and email.

Yes. Scores are graded against a fixed rubric and grounded in the transcript, policy and KB, and every criterion returns the model's reasoning. That makes results consistent across agents, channels and languages, and auditable, so a disputed score can be traced back to the exact moments that drove it.

GenAI extracts empathy cues, missed upsell hints and compliance gaps from each interaction, then auto-generates a personalized coaching agenda per agent, specific timestamps, what to reinforce and what to fix. Supervisors get an evidence-based plan instead of skimming a handful of calls.

Both. Real-time streaming scores interactions as audio flows in at sub-500ms for live supervisor alerts and barge-in cues, and an overnight batch mode grades the full corpus for trend reporting. You can run either or both per queue.

A language-agnostic policy engine checks disclosures, prohibited claims and keyword blacklists grounded in your policy. PII redaction and PCI masking run automatically, and secure reviewer mode lets sensitive interactions be audited without exposing raw data, backed by SOC 2 Type II, HIPAA, PCI and GDPR controls.