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May 25, 20260 citationsOpen Access

CogniClause: AI-Powered Contract Intelligence System for Clause-Level Risk Analysis and Optimization

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OMOm Rajesh MoreNKNitya Hitendrabhai KalolaAVAniket Nanasaheb Varpe

Key Points

  • This research aims to develop an AI-powered system for detailed analysis and optimization of contract clauses.
  • Utilized transformer architectures like Sentence-BERT and LegalBERT for clause-level analysis.
  • Performed semantic embedding generation and contextual risk classification.
  • Integrated multiple technologies including FastAPI, React, and Supabase for a comprehensive platform.
  • Achieved 88–92% accuracy in clause classification, indicating high effectiveness.
  • Demonstrated ~85% precision in risk detection across contracts.
  • Maintained an average processing time of 2–4 seconds per document.

Abstract

CogniClause is an AI-powered contract intelligence system designed to automate clause-level legal document analysis using modern Natural Language Processing and transformer-based semantic understanding techniques. The system performs: contract text extraction, clause segmentation, semantic embedding generation, contextual risk classification, and AI-assisted clause optimization. Unlike traditional keyword-based legal analysis systems, CogniClause leverages transformer architectures such as Sentence-BERT and LegalBERT to capture contextual meaning and improve risk detection accuracy in contractual clauses. The platform integrates: React frontend dashboard, FastAPI backend services, Supabase PostgreSQL database, PyMuPDF document parsing, and Large Language Models for intelligent clause rewriting and optimization. Key Features: Semantic clause analysis Risk scoring and classification AI-generated safer clause alternatives PDF/DOCX contract processing Interactive visualization dashboard Modular scalable architecture Technologies Used: Python FastAPI React Tailwind CSS Sentence Transformers LegalBERT Supabase PyMuPDF LLM APIs (OpenRouter/Groq) Experimental evaluation on real-world contract samples demonstrated: 88–92% clause classification accuracy ~85% risk detection precision 2–4 second average processing time This work explores the practical application of transformer-based Legal NLP systems for improving efficiency, accessibility, and consistency in contract review and legal risk analysis workflows.

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Cite This Study

More et al. (2026) studied this question.

synapsesocial.com/papers/6a13e8d20e02ee3982d33624https://doi.org/10.5281/zenodo.20355873
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