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@article{203379,
        author = {Ashwini Vishnu Gaikwad and Lina Bharat Nhayade and Chanchala Digambar Firake and Pranjal Sandip Shirsath and Punam Bajirao Wagh},
        title = {Schedulix AI: AI-Powered Corporate Appointment Booking System},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {12},
        pages = {11223-11230},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=203379},
        abstract = {Efficient appointment coordination is essential for modern organizations to maintain effective communication, op¬timal utilization of resources, and smooth operational workflow. Traditional scheduling approaches that depend on manual co¬ordination or basic digital tools often lead to booking conflicts, delayed confirmations, and increased administrative effort.
This paper presents an Artificial Intelligence–enabled Cor¬porate Appointment Booking System to automate enterprise scheduling processes. The proposed system integrates Artificial Intelligence and Natural Language Processing techniques to support conversational appointment requests, intelligent time-slot recommendations, automated approval workflows, and real-time monitoring of scheduled meetings.
The system is implemented using a full-stack architecture consisting of a React-based user interface, Spring Boot RESTful services, and PostgreSQL database management. An AI assistant powered by the Llama 3.2 model is incorporated to interpret user requests, analyze scheduling constraints, and generate context-aware appointment recommendations. Secure system access is maintained through role-based authentication and authorization mechanisms to ensure data protection across multiple user roles. Experimental evaluation demonstrates improvements in scheduling efficiency, reduced appointment conflicts, and en-hanced user interaction experience. The results indicate that AI-driven automation can significantly improve corporate appoint¬ment management and support the development of intelligent enterprise scheduling solutions.},
        keywords = {Artificial Intelligence, Appointment Scheduling, Corporate Automation, Llama 3.2, Spring Boot, ReactJS, JWT Security, Natural Language Processing},
        month = {May},
        }