Abstract
The increasing complexity and volume of legal contracts necessitate intelligent systems capable of automating contract analysis, verification, and risk assessment. Traditional manual review processes are time-consuming, error-prone, and often lack consistency, particularly when dealing with large-scale document collections. This paper presents a model referred as ContractIQ, an AI-based contract intelligence platform that leverages Natural Language Processing (NLP), GPT-4o-mini, and Retrieval-Augmented Generation (RAG) to enable automated clause extraction, semantic understanding, and legal risk detection. The proposed system processes contract documents, performs structured text extraction, and identifies key contractual clauses such as termination, liability, confidentiality, and payment terms. A proposed automated verification mechanism is introduced to detect missing clauses, ambiguous language, and high-risk contractual conditions using a quantitative risk scoring model. Furthermore, a semantic search framework based on vector embeddings enables efficient retrieval of contextually relevant information, while the RAG-based question answering module generates accurate, evidence-backed responses with traceable citations. Experimental evaluation demonstrates that the system achieves high performance in clause extraction, 92 % retrieval accuracy, and 1.8 sec of response time, significantly outperforming traditional approaches. The proposed platform reduces manual effort, enhances decisionmaking efficiency, and ensures improved transparency in contract analysis. This work contributes to the growing field of LegalTech by providing a scalable and intelligent solution for automated contract verification and risk assessment. Future enhancements will focus on domain-specific model optimization and privacy-preserving local deployment.
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