Built an AI-enhanced peer review platform designed to improve fairness and transparency in team-based evaluations. The system supports anonymous (single- and double-blind) reviews, configurable rubrics, and real-time feedback workflows, addressing issues like bias, inconsistent grading, and lack of structured evaluation processes . Developed a scalable full-stack architecture using React, Express, PostgreSQL, and Flask, incorporating features such as role-based access control, CAS-based authentication, real-time notifications, and instructor analytics dashboards. Additionally, integrated a GPT-powered AI service to analyze feedback for sentiment and toxicity, generate constructive rewrite suggestions, and summarize reviews, helping users produce more effective and professional evaluations . The platform is containerized with Docker, includes CI/CD pipelines, and follows enterprise-level practices such as rate limiting, audit logging, and data privacy compliance, making it production-ready.