Enables fact-checking of AI responses against reliable sources and validation of responses against document content to ensure accuracy and reliability.
Enables AI agents to add decision drafts, evidence, and counterarguments to a shared local decision state, while users confirm or reopen decisions in a web console. Prevents unverified agent answers from being silently turned into code.
A semantic retrieval system that gives AI assistants on-demand access to domain-specific governance principles — a queryable 'second brain' of encoded standards.
Provides real-time, privacy-preserving analysis of LLM interactions to detect problematic behaviors like medical advice, dangerous file operations, physics speculation, and unsupported claims. Recommends safety interventions and builds a taxonomy of LLM limitations through crowdsourced evidence collection.
Helps ground AI agents in reality by fact-checking responses against official documentation and reading project files to prevent hallucinations during coding sessions.