Level AI

Automatically analyzes incoming support tickets and routes them to the most appropriate agent or team based on content, complexity, and agent expertise. Uses AI to understand ticket intent and urgently level to minimize time spent on manual routing decisions.

Analyzes customer messages and support conversations in real-time to detect emotional tone, frustration levels, and sentiment shifts. Provides immediate alerts when sentiment becomes negative to enable proactive intervention.

Automatically detects customer language and provides translation capabilities for support conversations. Enables support teams to assist customers in multiple languages without requiring multilingual agents.

Automatically identifies tickets that need escalation based on complexity, customer value, or sentiment urgency. Routes escalations to appropriate teams and tracks escalation metrics to identify systemic issues.

Analyzes customer support conversations and feedback to identify mentions of competitors, feature requests, and market insights. Surfaces competitive threats and opportunities based on what customers are saying.

Generates automated health scores for customers based on support interaction patterns, sentiment trends, resolution times, and engagement frequency. Identifies at-risk customers before they churn and highlights expansion opportunities.

Unifies customer conversations across email, chat, messaging platforms, and other channels into a single view. Maintains conversation context and history regardless of which channel the customer uses, eliminating the need for separate integrations per channel.

Analyzes aggregated support conversations to identify recurring issues, common customer questions, and patterns in agent handling. Surfaces insights about what's causing support volume and where training or product improvements are needed.

Tracks and analyzes individual agent performance metrics including resolution time, customer satisfaction, sentiment impact, and conversation quality. Identifies top performers and areas for coaching or training.

Suggests pre-written or AI-generated responses to support tickets based on the ticket content and historical similar resolutions. Agents can accept, modify, or reject suggestions to speed up response time while maintaining quality.

Identifies bottlenecks and inefficiencies in the support process and recommends optimizations to reduce average handle time. Tracks improvements over time as changes are implemented.

Collects and aggregates customer satisfaction feedback from support interactions and tracks satisfaction trends over time. Correlates satisfaction with specific agents, issue types, and resolution approaches.

Connects to company knowledge bases and documentation, suggesting relevant articles or resources to agents as they handle tickets. Helps agents find answers faster and can be shared directly with customers.