Multi-AI Consensus Tool: Query multiple AI models in parallel, synthesize responses for better accuracy, and reduce AI bias through ensemble decision-making.
Provides advanced evaluation tools for assessing AI safety, alignment, and performance of LLM outputs. Enables programmatic evaluation of quality, safety metrics like toxicity and PII detection, and operational metrics including carbon footprint and cost estimation.
Enables multi-model consensus decision-making for high-stakes AI decisions, using independent expert models and a judge to surface disagreements and ground decisions.
Prediction stability engine for AI agents. Evaluate model stability, detect ghosts, probe any LLM for instability, monitor fleet drift. 20 tools + 1 resource. Works with Claude, Cursor, VS Code.
Enables LLM-as-judge text quality evaluation via MCP, scoring original-revised pairs with multi-axis weighted rubrics, testing scorer models against labeled golden sets, and cross-validating agreement between two scorers.
An MCP server that audits LLM-as-judge evaluations, detecting judge drift across runs, measuring bias through controlled probes, and comparing judge agreement with human raters.