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Automate document evaluation by decomposing content against criteria with cognitive AI agents and ontology-grounded scoring. The modular pipeline ingests documents, aligns sections, spawns evaluators, scores evidence, debates findings, and generates a final verdict report.

Ontology-grounded evidence scoring uses AI agents that maintain state as OWL ontologies to reason about document sections and criteria. Each evaluated claim is backed by direct quotes or citations, ensuring transparent justification and auditability for clinical or policy documents.

Evaluate policy proposals or clinical reports by running the full pipeline workflow on a sample document. The system applies deterministic scoring or Claude-assisted subagents to generate a score, ontology state, and final verdict with transparent justification.

Yes, a panel of AI agents maintains state as OWL ontologies to reason about sections and evaluation criteria. This agent-ontology evaluation approach supports deterministic evidence scoring and structured debate across the evaluation pipeline.

The pipeline supports both deterministic scoring and Claude-assisted subagents for document evaluation. It enforces a frontmatter-driven entry point and integrates optional scripts, references, and assets while orchestrating ingest, criteria loading, alignment, scoring, and reporting.

Use ontology-driven document evaluation for essays, policies, contracts, and clinical reports requiring evidence-based verdicts with auditability. It replaces manual review when you need structured scoring, transparent justification, and multi-agent debate across complex evaluation criteria.