Level AI

Level AI features automated QA (QA-GPT) that evaluates every interaction against custom scorecards, tagging moments, and scoring calls to replace manual sampling. This delivers consistent, auditable quality metrics and frees analysts to focus on coaching and remediation instead of time-consuming manual reviews.

It provides immediate, contextual prompts, knowledge snippets, and next-best-action suggestions during live interactions, reducing handle time and improving first-contact resolution while supporting agents with timely cues instead of post-call feedback.

This feature aggregates sentiment and semantic themes across channels to reveal root causes, trends, and iCSAT (Inferred Customer Satisfaction) scores. These insights enable prioritized fixes, product feedback loops, and evidence-based decisions grounded in customer conversations.

It helps capture agent screens and interactions for context-rich coaching; paired coaching workflows assign targeted training based on QA findings. This helps supervisors convert data into measurable performance improvements.

This feature handles scripted voice and chat tasks with semantic understanding and actions, enabling automated handling for common queries and reducing volume routed to human agents while preserving escalation paths for complex cases.