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@article{202477,
        author = {Dr. Shah Saloni Niranjan and Kakde Riya Rajesh and Kokate Mansi Sanjay and Shinde Dipali Apparao},
        title = {ARCHITECT AI BASED PROJECT TRACKER AND BUDGET ESTIMATOR},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {12},
        pages = {7428-7432},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=202477},
        abstract = {Construction projects today demand far more than skilled design — they require seamless coordination of finances, timelines, and visual communication across multiple stakeholders. Yet most teams still juggle spreadsheets alongside disconnected 3D viewers, a combination that routinely leads to missed budget warnings and delayed decisions. This paper presents the Architect AI-Based Project Tracker and Budget Estimator, a web platform built to close that gap. The system combines Babylon.js-powered real-time 3D model interaction with machine learning-driven cost forecasting, creating a live digital twin of a construction site. At its core is Archie, an AI agent built on Google Vertex AI, which handles natural-language queries about project health, budget trends, and risk exposure, providing both architects and clients with instant, interpretable answers without waiting for manual reports. Early testing shows the system achieving 90–95% accuracy in overrun prediction and a 92% synchronization rate for 3D model updates, suggesting meaningful practical value for teams managing complex builds.},
        keywords = {AI-Driven Agent, Generative AI, Babylon.js, 3D Visualization, NLP, Web Application, Java, Spring Boot, Python Microservices, Budget Forecasting, Risk Assessment},
        month = {May},
        }