Enthu.AI

Enthu.AI's core Auto QA capability uses generative AI to evaluate 100% of customer conversations without manual intervention. The system scores calls against customizable evaluation criteria including empathy, compliance language, script adherence, and resolution effectiveness. Unlike legacy keyword-spotting approaches, the GenAI engine interprets conversational context and intent, enabling more nuanced scoring. QA managers can define weighted scorecards tailored to their specific evaluation frameworks and update criteria without engineering support.

The platform deploys modular AI agents — Transcript, QA, Reporting, RPA, Compliance, CSAT, and Real-Time Assistance — each designed for a specific contact center function. This architecture allows organizations to activate only the agents they need, avoiding the all-or-nothing deployment model of monolithic platforms. Each agent operates on shared conversation data but produces independent outputs, enabling teams to start with QA automation and incrementally add compliance monitoring, sentiment analysis, or workflow automation as needs evolve.

Enthu.AI transcribes every customer conversation with speaker diarization (separating agent from customer speech) and precise timestamps. The transcriptions support 100+ languages and serve as the foundation for all downstream analysis including QA scoring, compliance checks, and sentiment detection. Speaker-labeled transcripts enable moment-level navigation so supervisors can jump directly to specific segments of a conversation without listening to the full recording.

Described as a 'Spotify-like' experience, the call library lets supervisors curate playlists of exemplary or problematic call segments that agents can review independently. This self-service coaching approach reduces dependency on scheduled one-on-one sessions and enables agents to learn at their own pace. Playlists can be organized by topic, skill gap, or compliance issue, and agents can bookmark moments for follow-up discussion with their supervisor.

The Sentiment Analysis and CSAT Agent work together to measure the emotional dynamics of customer conversations, flagging escalation risks and identifying drivers of customer satisfaction or dissatisfaction. The system detects sentiment shifts throughout a conversation, highlighting moments where customer tone changes from positive to negative or vice versa. This enables proactive intervention by supervisors on at-risk calls and post-interaction analysis of what drives positive or negative customer experiences across the full call volume.