Ontop - An Intelligent Workspace Management Application with Machine Learning Integration

Abstract

In today’s fast moving digital environment, managing the large number of clients, contacts and projects is a problem faced by professionals throughout the world. Existing solutions focus on a specific technique for boosting productivity, such as automated note-taking or To-Do list generation. This paper presents Ontop, a personal workspace management application that combines multiple known productivity tools into one. In a single interface, it allows for client detail storage, To-Do list, Project management and event logging. It supports real-time file access, contextual client interactions, and seamless organization of work data. By providing major productivity tools in a single interface, it increasing efficiency and improving UX. Ontop also uses its own ML layer (built with Random Forest, BERT-based transformer models and Neural Ranking Model) which allow smart contact-based notifications which show relevant project information when receiving a call from a client. This layer also helps with task and note recommendations. This paper describes the motivation, system architecture, feature implementation, and future scope of Ontop, and how it can be used to enhance productivity.

Introduction

In today’s digital, high-workload environment, freelancers, remote workers, and small business professionals often manage multiple clients, projects, tasks, and meetings simultaneously. Existing productivity tools are usually single-purpose or team-oriented, leaving a gap for an integrated personal workspace manager. To address this, Ontop is introduced—a cross-platform mobile application designed to centralize personal workspace management. Ontop allows users to organize clients, projects, tasks, events, and files within one interface while maintaining smooth cross-device data sync.

The application is built with Flutter for a fast and native-like frontend, Node.js/Express.js for API operations, and MongoDB Atlas as a scalable NoSQL cloud database. It also uses local SQLite for instantaneous UI updates, synchronizing changes in the background. A key innovation is Ontop’s machine learning layer, which analyzes user activity, project data, notes, and communication patterns. This ML layer generates contextual summaries of ongoing work with each contact, which are pushed to the user during incoming calls to improve multitasking and situational awareness.

Literature Review

Prior studies highlight the importance of minimalistic UI design, moderated user engagement, and AI-enhanced features in productivity applications. Deep learning and NLP have proven effective for recommendation systems, intent detection, and extracting action items from unstructured communication. Research also emphasizes multimodal context awareness, agile UI development practices, and the increasing need for intelligent organizational tools among hybrid and freelance workers.

Methodology

Ontop’s system uses:

• Flutter for cross-platform UI with fast rendering and customizable widgets.

• Node.js/Express.js for efficient, asynchronous API handling.

• MongoDB Atlas with user-specific collections for scalable and flexible data storage.

• RESTful API architecture for easy integration and future expansion.

• Python-based ML microservices, including Random Forest models, BERT NLP pipelines, and ranking models for call-context generation.

The app workflow includes secure authentication, tab-based data loading, local-first updates via SQLite, and ML-powered call notifications. NLP automatically extracts tasks and insights from user notes, while priority prediction and context summarization improve productivity and responsiveness.

System Workflow and Features

• User authentication with secure hashing

• Real-time synchronization between SQLite (local) and MongoDB (cloud)

• Context-aware call alerts showing relevant projects, deadlines, and inferred discussion topics

• Integrated communication shortcuts (WhatsApp, email, phone)

• Intelligent task suggestions based on user behavior patterns

Testing and Results

Performance testing revealed that using a local-first approach reduces UI delay significantly—from ~340 ms to ~80–110 ms—resulting in smoother interactions. The final implementation successfully combines contacts, notes, tasks, events, and projects into a unified workspace, reducing app-switching overhead and improving user productivity through interconnected data structures and AI-assisted features.

Conclusion

The paper presented Ontop a personal workspace manager created to be a facilitator of task management with the potential for increased productivity. It employs a Node.js/MongoDB back-end and a Flutter front-end, incorporating real-time data capture/processing and modular design for backwards/forwards compatibility and ease of support and maintenance. The current iteration of Ontop meets all necessary CRUD requirements for personal use within the professional arena as well as task management integration. Furthermore, to make Ontop more than just a static management tool but an intelligent assistant, various ML and NLP modules work in conjunction with Ontop, for example. They also help ensure user patterns become predictive, adjustable and contextually appropriate. Future iterations of Ontop will include web-based dashboard access, client-sided analytics and encrypted cloud saves to further facilitate usability and data protection/access. Ultimately, Ontop represents a very practical addition to modern workspace management for increasingly demanding intelligent, accessible and secure productivity tools.

References

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Copyright

Copyright © 2025 Madhumati Pol, Alesha Mulla, Ajinkya Ghule, Aliza Kundal. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.