Quotient AI offers a state-of-the-art detection platform designed to monitor AI applications. It helps users instantly track issues such as hallucinations, RAG failures, and performance problems with just a few lines of code, providing real insights from real usage through automated monitoring dashboards and detailed diagnostics.

Key Features:

• Instant failure detection (e.g., hallucinations, RAG failures)
• Automatic root cause analysis
• AI-powered recommendations for fixes
• Easy integration with Python or Typescript SDKs (a couple lines of code)
• Detailed dashboards for debugging, showing full model responses and tracing issues to the source

Use Cases:

• Monitoring AI applications for performance and reliability
• Detecting and diagnosing AI failures before they impact users
• Conducting fast experimentation cycles for generative models and retrieval systems
• Ensuring policy compliance of AI agents (as highlighted by Wayfair's use case)
• Shipping high-quality AI products through fast experimentation cycles

Benefits:

• Catch AI failures before users do
• Track issues with minimal code integration
• Automatically detects failures, identifies root causes, and suggests fixes
• Enables shipping reliable AI like any other piece of code
• No manual labeling or judge model setup required
• Provides document-level evidence and clear reasoning for issues
• Helps teams build more reliable AI applications through cutting-edge research and practical tools