Authors - Tanmoy De, Vimal Kumar, Pratima Verma Abstract - The process of operating modern engineering companies is often compartmentalized due to the straightforward nature of the operations requirements that mani-fest themselves within the realm of the software creation and hardware manufacturing. The absence of integration between Agile practices and Waterfall lifecycles is a waste of administrative resources and delays time-to-market. A hybrid project management SaaS is offered in this project called Converge, which will target the integration of these areas without sacrificing the integrity of the data stored in digital code repositories and physical Bill of Materials (BoM). The adoption of Multi-Modal Documentation, Real-time State Synchronization and IoT-oriented Task Automation have their measures of efficiency of workflow, responsiveness of interface, and cross-domain data consistency. The most recent breakthroughs in Natural Language Processing (NLP) and Computer Vision are used to make the experience more practical; a custom AI pipeline based on the ResNet50 and LSTM networks are able to extract visual storyboards of technical video reports with an impressive F Score of 83.00% (with 79.20% Precision and 86.50% Recall), and Transformer based models (including BART) are able to generate structured textual summaries with the leading ROUGE-L score of 0.42. The system is anchored on a dynamic split-brain architecture to display coherent information in either Kanban boards or Gantt charts as the case arises. Status updates increase exponentially with integrated IoT triggers to computerize the execution of tasks via a direct hardware to software communication. The survey is based on the trade offs between the flexibility of UI, the complexity of the database schema, and the latency of the API to compare the old siloed tools to this new hybrid framework. The future of engineering management relies on new tendencies, such as Hybrid Machine Learning, to predictively allocate resources, cutting the error rates in estimating the effort by three times (MMRE to 0.32) with the help of such dominant historical measures of resources as Lines of Code (feature importance score of 0.73) and automated reporting of resource depend-ency. Finally, it is demonstrated that the suggested architecture with the support of a CNN optimized backend video storage, which will save 61.80% of the time at a small cost of 2.30% BDBR, will save about 60% of time on manual docu-mentation and synchronize assets in real-time with a latency less than 200ms (2 seconds).
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