AI-Assisted Startup Readiness Assessment and Decision Support System Using Semantic Similarity and Personalized Recommendations

Author

Amudala Ankitha, Kunchala Srinivas Rao

Abstract

 Assessing the readiness of an early-stage startup requires consideration of several factors related to its problem, market, product, business model, team, resources, finances, and growth plans. Existing startup evaluation approaches often concentrate on idea validation or prediction of future success, with comparatively less emphasis on diagnosing the startup’s current level of readiness and identifying areas that require improvement.

This paper presents an AI-Assisted Startup Readiness Assessment and Decision Support System that combines structured assessment, data analytics, natural language processing, semantic similarity analysis, and generative artificial intelligence. The implemented Startup Readiness Assessment Framework (SRAF) contains 20 questions organized into eight dimensions: Problem and Innovation, Market, Product, Business Model, Founder and Team, Resources, Financial, and Growth and Incubation. Assessment responses are processed through a deterministic scoring engine to calculate dimension-level scores, Startup Readiness Score (SRS), Founder Readiness Score (FRS), Overall Readiness Score (ORS), and the corresponding readiness level.

The system additionally performs risk analysis and provides explainable assessment insights, benchmarking, and embedding-based semantic similarity analysis for identifying conceptually related startup profiles. A generative-AI component uses the assessment and analytical outputs to produce personalized improvement areas, recommended next steps, and incubation-support suggestions. The platform also includes interactive dashboards, assessment history, progress tracking, and PDF report generation.

The resulting platform integrates startup readiness assessment and AI-assisted decision support into a single workflow. Rather than predicting whether a startup will succeed in the future, the system focuses on evaluating its present readiness, highlighting areas that require attention, and providing actionable guidance to support founders and incubators.

Keywords

Startup Readiness Assessment, Artificial Intelligence, Data Science, Decision Support System, Natural Language Processing, Semantic Similarity, Sentence Embeddings, Recommendation System, Generative AI, Startup Analytics

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References

Ambarita, N., & Suwatno. (2024). Entrepreneurial education and readiness: A bibliometric analysis. Jurnal Manajemen Universitas Bung Hatta, 19(1), 68–80. https://doi.org/10.37301/jmubh.v19i1.24347

Bangdiwala, M., Mehta, Y., Agrawal, S., & Ghane, S. (2022). Predicting success rate of startups using machine learning algorithms. Proceedings of the 2nd Asian Conference on Innovation Technology (ASIANCON), 1–6.
https://doi.org/10.1109/ASIANCON55314.2022.9908921

Choi, Y. (2024). Startup success prediction with PCA-enhanced machine learning models. Journal of Technology Management & Innovation, 19(4). https://doi.org/10.4067/S0718-27242024000400077

Coduras, A., Saiz-Álvarez, J. M., & Ruiz, J. (2016). Measuring readiness for entrepreneurship: An information tool proposal. Journal of Innovation & Knowledge, 1(2), 99–108. https://doi.org/10.1016/j.jik.2016.02.003

Dellermann, D., Lipusch, N., Ebel, P. A., Popp, K. M., & Leimeister, J. M. (2017). Finding the unicorn: Predicting early-stage startup success through a hybrid intelligence method. Proceedings of the 38th International Conference on Information Systems (ICIS), Seoul, South Korea, 1–12. https://doi.org/10.48550/arXiv.2105.03360

Duc, A. N., Dahle, Y., Steinert, M., & Abrahamsson, P. (2017). Towards understanding startup product development as effectual entrepreneurial behaviors. Lecture Notes in Business Information Processing, 304, 199–204. https://doi.org/10.1007/978-3-319-69191-6_15

Fayyaz, Z., Ebrahim, M., Nawara, D., Ibrahim, A., & Kashef, R. (2020). Recommendation systems: Algorithms, challenges, metrics, and business opportunities. Applied Sciences, 10(21), 7748. https://doi.org/10.3390/app10217748

Maarouf, A., Bakiaj, A., & Feuerriegel, S. (2026). Predicting startup success using large language models: A novel in-context learning approach. arXiv preprint arXiv:2601.16568. https://doi.org/10.48550/arXiv.2601.16568

Maarouf, A., Feuerriegel, S., & Pröllochs, N. (2025). A fused large language model for predicting startup success. European Journal of Operational Research, 322(1), 198–214. https://doi.org/10.1016/j.ejor.2024.09.011

Potanin, M., Chertok, A., Zorin, K., & Shtabtsovsky, C. (2023). Startup success prediction and VC portfolio simulation using CrunchBase data. arXiv preprint arXiv:2309.15552, 1–13. https://doi.org/10.48550/arXiv.2309.15552

Prajapati, A. R. C., & Albert, D. W. (2025). IdeaSense: AI-driven framework for startup idea assessment and validation. International Journal of Innovative Research in Computer and Communication Engineering, 13(10).

Sompura, D. U., Jain, P., & Serene, I. M. (2024). Start-up success prediction analysis using hybrid machine learning technique. International Journal of Intelligent Systems and Applications in Engineering, 12(2), 10845–10856.

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