AI Document Intelligence

Configured extraction templates run directly on scored call transcripts or documents through post-analysis extraction and classification. The system captures structured information from analyzed interactions, allowing teams to translate unstructured text into actionable data points. These outputs can then trigger automated workflow actions or update connected platforms such as CRMs and internal dashboards.

Yes, teams can define custom QA scorecards, rubrics, metrics, and weights to match their specific organizational standards. The AI evaluates written interactions and documents consistently against these criteria, providing metric-level explanations for every score so reviewers and managers understand how evaluations were determined without manual guesswork.

The AI continuously monitors analyzed interactions and documents for critical issues such as privacy violations, improper advice, hostile agent behavior, and customer escalation risks. When a potential violation is detected, the platform flags the incident and can trigger automated alerts or workflow notifications, enabling supervisors to conduct prompt reviews.

Pro tier features include targeted agent coaching generated from recurring performance patterns, structured training programs, lessons, quizzes, and knowledge checks. Agents can also practice handling challenging customer scenarios using AI mock-caller roleplay sessions, allowing teams to track improvement progress and link quality assurance metrics directly to skill development.

EmberQA supports ingestion and API setup to facilitate data exchange with external tools, including customer relationship management systems, ticketing systems, internal reporting dashboards, and webhooks. These integrations allow teams to automate post-interaction workflows, verify captured customer details against system records, and export reporting data for cross-departmental visibility.

Managers gain access to real-time QA dashboards, agent performance analytics, performance trends across teams and locations, CSV exports, PDF trend reports, and daily AI QA summaries. These reporting tools track recurring quality issues, evaluate coaching effectiveness, and provide clear visibility into interaction quality across all supported channels.