Agentic AI platform for contact center intelligence and conversation analytics to improve customer service operations.
Agentic AI platform for contact center intelligence and conversation analytics to improve customer service operations.
Enthu.AI is an agentic AI platform purpose-built for contact centers, combining conversation intelligence, automated quality assurance, and agent coaching into a unified workflow. The product is designed to replace the manual, sample-based QA process that most contact centers still rely on — where supervisors review only 2–5% of calls — with a fully automated GenAI-driven evaluation that scores 100% of customer interactions across voice and digital channels. By analyzing every conversation, Enthu.AI surfaces compliance gaps, sales objections, sentiment shifts, and coaching opportunities that would otherwise remain invisible.
At the core of the platform is its Auto QA engine, which uses generative AI to evaluate calls against custom scorecards without manual intervention. Supervisors define the questions and rubrics they care about — script adherence, disclosure language, soft skills, objection handling — and the AI scores each interaction with reasoning that humans can audit. Around this engine sits a stack of supporting features: AI-powered transcriptions with speaker diarization, post-call AI summaries, sentiment analysis to gauge customer pulse, and a reporting layer that highlights trends across teams, agents, and call drivers. A 'Call Library' feature lets managers curate Spotify-style playlists of exemplary calls so agents can self-coach on demand.
Enthu.AI targets regulated and high-volume verticals where conversation quality directly affects revenue and risk: financial services and lending, insurance, healthcare, and utilities. The platform integrates with major CCaaS, dialer, and CRM systems so calls flow in automatically and insights flow back into agent workflows. Use cases extend beyond QA into sales performance optimization, compliance monitoring, appointment booking analytics, and structured agent coaching programs. Enterprise-grade security is positioned as a first-class concern, reflecting the data sensitivity of the industries it serves.
The 'agentic AI' positioning reflects the platform's 2026 direction — moving from passive analytics that report what happened to AI agents that take action: flagging at-risk calls in real time, generating coaching plans for specific agents, and assisting frontline reps live during conversations. Recent content from the company highlights real-time agent assist copilots that improve First Call Resolution (FCR) and Average Handle Time (AHT), signaling a shift toward in-the-moment intervention rather than after-the-fact review. For contact center operations leaders, Enthu.AI is pitched as a way to scale QA coverage from a sliver to the entirety of customer interactions while freeing QA analysts and supervisors to focus on coaching rather than scoring.
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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.
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Enthu.AI's 2026 push centers on agentic AI and real-time intervention. The company is publishing actively on Real Time Agent Assist copilots that boost FCR and AHT, signaling a strategic move from retrospective Auto QA into live in-call assistance. Recent content also clarifies the distinction between conversation intelligence and traditional speech analytics, positioning Enthu.AI on the GenAI-native side of that line. Expect deeper integration between post-call insights and live coaching loops — where Auto QA findings automatically inform the prompts the real-time copilot surfaces to agents — and continued investment in vertical-specific scorecards for financial services, insurance, healthcare, and utilities.
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