POLICYIQ: AN AI-POWERED POLICY CONFLICT VERIFICATION SYSTEM

Organisations, governments and regulated industries operate under large, continuously revised bodies of policy. As these documents evolve across versions, editions and jurisdictions, overlapping, contradictory or silently modified clauses accumulate and create compliance, governance and legal risk that manual review cannot scale to detect. To resolve these systemic bottlenecks, this paper presents PolicyIQ, an enterprise-grade artificial intelligence platform for automated policy conflict verification and version-aware change intelligence. PolicyIQ ingests heterogeneous policy documents through a robust extraction pipeline with optical character recognition (OCR) fallback, decomposes them into atomic clauses using a hybrid rule-based and semantic segmentation engine, and encodes each clause into a 384-dimensional dense vector using a Sentence-BERT transformer (MiniLM-L6). A cross-version cosine-similarity matrix drives a multi-stage classifier that labels every clause transition as duplicate, modified, new, removed or direct-conflict, augmented by deontic, numeric and negation-scope heuristics for typed contradiction detection. A distribution-aware adaptive thresholding scheme replaces brittle fixed cut-offs by deriving decision boundaries from the per-corpus similarity distribution. Built on a decoupled four-tier microservices architecture utilising a FastAPI security gateway, asynchronous Celery workers, PostgreSQL relational storage and a Qdrant/FAISS vector store, PolicyIQ delivers explainable dual-audience summaries while maintaining data privacy through JWT authentication and role-based access control. On a controlled synthetic policy corpus the platform attains target performance of 91.6% precision for adaptive conflict classification and 0.944 segmentation F1, with sub-second clause-level inference, demonstrating a reproducible, provenance-aware pathway from raw policy text to actionable conflict intelligence.