Degree2Startup: Personalized Startup Idea, Government Scheme & Document Guide System

Abstract

Entrepreneurship is a key driver of economic growth, innovation, and employment generation in India. Despite numerous government initiatives such as Startup India, Atal Innovation Mission, and Ministry of Micro, Small and Medium Enterprises (MSME), a significant number of students and graduates struggle to convert their academic knowledge into viable business ventures. The major challenges include lack of personalized startup guidance, limited awareness of applicable government schemes, documentation complexity, and fragmented information sources.Degree2Startup is a web-based intelligent guidance system designed to bridge the gap between education and entrepreneurship. The platform analyzes a user’s academic background, skills, and interests to recommend personalized startup ideas across domains such as Technology, Agriculture, Healthcare, and Service sectors. It further maps eligible government schemes, funding programs, subsidies, and incubation opportunities relevant to the user’s profile. The system integrates a structured backend architecture using Spring Boot and database management systems such as MySQL or MongoDB to manage user data, startup ideas, and scheme information securely. It includes secure authentication, automated checklist generation for legal and business registration documents, and dynamic PDF report generation. An administrative panel allows real-time content updates to ensure accuracy and scalability. The proposed framework aims to serve as a digital startup mentor for students, recent graduates, and aspiring entrepreneurs—especially in semi-urban and rural regions—by providing actionable insights, structured guidance, and simplified access to entrepreneurial resources while maintaining data security and usability standards.

Introduction

Entrepreneurship is an important driver of economic growth, innovation, and employment in India. Government initiatives such as Startup India and Atal Innovation Mission have strengthened the startup ecosystem by offering funding, incubation, tax benefits, and mentorship. However, many students and graduates—especially from semi-urban and rural areas—struggle to convert their academic qualifications into practical business ventures due to limited awareness of opportunities, complex documentation procedures, and lack of structured startup guidance.

Traditional support systems such as mentorship programs, incubators, and online resources are often difficult to access or fragmented. Existing government portals mainly provide static information without personalization, requiring users to manually search for relevant schemes and interpret eligibility requirements. Rule-based recommendation systems have improved information filtering but still lack adaptability and deeper personalization.

To address these limitations, the Degree2Startup platform is proposed as an intelligent digital assistant that provides structured startup guidance. It analyzes users’ academic background, skills, and interests to recommend suitable startup ideas, map relevant government schemes, and generate customized documentation checklists. The system uses a layered architecture with a user interface, recommendation engine, backend developed with Spring Boot, and a database using MySQL or MongoDB.

The methodology includes user profiling, rule-based startup idea recommendation, government scheme mapping, and automated document checklist generation. By integrating these features into a single platform, Degree2Startup aims to simplify decision-making, improve awareness of government support programs, and promote self-reliant entrepreneurship, particularly for students and first-time entrepreneurs.

Conclusion

The proposed Degree2Startup framework demonstrates that intelligent recommendation systems can serve as an effective assistive tool for structured entrepreneurial guidance, particularly for students and first-time founders with limited business exposure. The achieved recommendation accuracy, balanced precision and recall, and significant reduction in response time indicate that the system is both technically reliable and practically viable. The integration of profile analysis, rule-based recommendation logic, scheme mapping, and automated PDF report generation provides a complete and deployable startup advisory platform. The results suggest that AI-driven entrepreneurial guidance systems can reduce informational barriers, simplify decision-making, and enhance access to structured startup support. While the system is not intended to replace professional financial or legal consultation, it offers meaningful assistance for preliminary startup planning and opportunity exploration.

References

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Copyright

Copyright © 2026 Prof. Abdul Kalam, Om Dakhane, Aditi Joshi, Vaishnavi Chavhan, Pritesh Kubde, Ansar Amhad. 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.