Verifies AI outputs in real-time across 6 dimensions, with automatic retry and failover to ensure correct, complete, and reliable LLM responses before they reach the user's editor.
An MCP server that lets any AI agent evaluate RAG outputs -- faithfulness scoring, hallucination detection, and retrieval quality metrics -- with zero API keys, using MCP sampling.
Enables fact-checking of AI responses against reliable sources and validation of responses against document content to ensure accuracy and reliability.
Behavioral governance layer for AI assistants that monitors for hallucination, inconsistency, and unsafe reasoning patterns while managing stateful AI sessions.
Exposes a deterministic AI-slop scanner and RAG grounding grader as MCP Tools, Resource, and Prompt, enabling any MCP client to evaluate text quality and context faithfulness.
Enables AI assistants to crawl, index, and retrieve information from technical documentation using semantic search, with optional knowledge graph validation for code hallucination detection.
Helps ground AI agents in reality by fact-checking responses against official documentation and reading project files to prevent hallucinations during coding sessions.