IRO Journals
Journal of Information Technology and Digital World

AI Startup Idea Validator

Open Access
Published: 23 September, 2026
Pages: 308-322
  • Krishna Rao P. , Krishna Rao P.
    Student, Department of Computer Science and Engineering, Geethanjali College of Engineering and Technology (Autonomous), Hyderabad, Telangana, India
    Student, Department of Computer Science and Engineering, Geethanjali College of Engineering and Technology (Autonomous), Hyderabad, Telangana, India
  • Aparna B. , Aparna B.
    Student, Department of Computer Science and Engineering, Geethanjali College of Engineering and Technology (Autonomous), Hyderabad, Telangana, India
    Student, Department of Computer Science and Engineering, Geethanjali College of Engineering and Technology (Autonomous), Hyderabad, Telangana, India
  • Sravani K. , Sravani K.
    Student, Department of Computer Science and Engineering, Geethanjali College of Engineering and Technology (Autonomous), Hyderabad, Telangana, India
    Student, Department of Computer Science and Engineering, Geethanjali College of Engineering and Technology (Autonomous), Hyderabad, Telangana, India
  • Yasaswini S. Yasaswini S.
    Student, Department of Computer Science and Engineering, Geethanjali College of Engineering and Technology (Autonomous), Hyderabad, Telangana, India
    Student, Department of Computer Science and Engineering, Geethanjali College of Engineering and Technology (Autonomous), Hyderabad, Telangana, India
view PDF
view PDF

How to Cite

P., Krishna Rao, Aparna B., Sravani K., and Yasaswini S. 2026. “AI Startup Idea Validator”. Journal of Information Technology and Digital World 8 (4): 308-22. https://doi.org/10.36548/jitdw.2026.4.002.
ACM ACS APA ABNT Chicago Harvard IEEE MLA Turabian Vancouver AMA

Download Citation

Endnote/Zotero/Mendeley (RIS) BibTeX

Keywords

Large Language Model Reasoning
Startup Viability Assessment
Business-Contextual Analysis
Financial Sustainability Modeling
Automated Decision Support
Prompt-Structured Evaluation
Persistent Evaluation Analytics

Abstract

Start-up validation at the initial stage demands a combination of business feasibility assessment, market environment, competitive positioning, technical feasibility, and financial sustainability, which is hard to accomplish manually. The proposed paper discusses a startup evaluation framework that combines Large Language Model (LLM) reasoning with business and financial analytics. The startup inputs such as problem description, target market, solution, value proposition, and business model are checked using the Flask backend and converted into structured prompts for LLM. The evaluation process will cover demand in the market, competition, uniqueness, scalability, feasibility, risk, and yield criterion-level results, viability ranking, and advice on how to improve. The finance module will handle income, profit margins, cash flow, burn rate, financing, leverage, cost per acquired customer, and life-time value to determine the financial health and viability of the start-up business. Persistence using MongoDB will allow for comparison with past evaluations.

References

  1. Kleinert, Simon, and Diemo Urbig. "Startup Evaluations with Generative Artificial Intelligence: An Exploratory Study on Early-Stage Investments and Survival Predictions by Large Language Models." Entrepreneurship Theory and Practice, 2026, 1-43.
  2. Razaghzadeh Bidgoli, Mona, Iman Raeesi Vanani, and Mehdi Goodarzi. "Predicting the Success of Startups using a Machine Learning Approach." Journal of Innovation and Entrepreneurship, 20254, vol. 13, no. 1: 80.
  3. Preuveneers, Jack, Joseph Ternasky, Fuat Alican, and Yigit Ihlamur. "Reasoning-based AI for Startup Evaluation (raise): A Memory-Augmented, Multi-step Decision Framework." ArXiv Preprint, 2025, arXiv:2504.12090.
  4. Kim, Jongwoo, Hongil Kim, and Youngjung Geum. "How to Succeed in the Market? Predicting Startup Success using a Machine Learning Approach." Technological Forecasting and Social Change, 2023, vol. 193: 122614.
  5. Li, Yisheng, Iman Zadehnoori, Ahmad Jowhar, Sean Wise, Andre Laplume, and Morteza Zihayat. "Learning from Yesterday: Predicting Early-Stage Startup Success for Accelerators through Content and Cohort Dynamics." Journal of Business Venturing Insights, 2024, vol. 22: e00490.
  6. Wei, Chih-Ping, Evana Szu-Han Fang, Chin-Sheng Yang, and Pin-Jun Liu. "To Shine or not to Shine: Startup Success Prediction by Exploiting Technological and Venture-Capital-Related Features." Information & Management, 2025, vol. 62, no. 6: 104152.
  7. Ahlgren, Thea Lovise, Helene Fønstelien Sunde, Kai-Kristian Kemell, and Anh Nguyen-Duc. "Assisting Early-Stage Software Startups with LLMs: Effective Prompt Engineering and System Instruction Design." Information and Software Technology, 2025, vol. 187: 107832.
  8. Choi, Youngkeun. "Startup Success Prediction with PCA-Enhanced Machine Learning Models." Journal of Technology Management & Innovation, 2024, vol. 19, no. 4: 77-88.
  9. Rady, Judy, David Townsend, and Rick Hunt. "From Algorithmic Hallucinations to Alien Minds: Addressing the Ideator's Dilemma through Entrepreneurial Work." Journal of Business Venturing, 2026, vol. 41, no. 1: 106550.
  10. Pan, Chengen, Xiaohong Pan, and Li Sun. "A Multi-Layer AI Decision Support System for Startup Success Prediction and Risk Assessment using Knowledge Graphs and Federated Learning." Scientific Reports, 2026, vol. 16: 1-46.
  11. H. Gunduz, “Kickstarter 2018 (Pre-Processed Data),” Zenodo, 2024. https://zenodo.org/records/12735215