Advanced Evaluationv1.0.0

Automate grading of AI assistant responses for factual accuracy, instruction following, and safety. Use direct scoring with calibrated rubrics to flag poor-quality outputs.

Compare two versions of a conversational AI on subjective criteria like tone and persuasiveness using pairwise comparison with position bias mitigation.

Implement an LLM-as-judge system to score student essays on clarity, argument strength, and evidence use, reducing human grading workload.

Detect bias in automated content moderation by analyzing disagreement patterns between LLM judges and human reviewers, focusing on systematic errors.

Build a production-grade pipeline that continuously evaluates retrieval-augmented generation outputs using direct scoring for answer relevance and factual accuracy.

Offer API-based LLM evaluation services for companies building AI applications. Charge per evaluation call with tiered pricing based on volume and rubric complexity.

Provide a dashboard for teams to design rubrics, run evaluations, and track metric trends. Monthly or annual subscription with seats and evaluation credit tiers.

Perform in-depth bias analysis on client evaluation systems using the bias mitigation protocols. Deliver reports with remediation recommendations.