Support organizations for early-stage startups receive grievances from founders about hiring, marketing, legal compliance, funding, and mentorship, and most of them still sort and assign these grievances by hand. This paper describes Grievance AI: Startup Ecosystem Intelligence Hub, a web platform that automates the first steps of that work. Grievance text is cleaned and converted into term frequency–inverse document frequency (TF-IDF) vectors, and a Support Vector Machine (SVM) or an XGBoost classifier assigns one of six categories. The predicted category selects the responsible officer, and the grievance then moves through a tracked lifecycle from submission to closure. Founders, officers, and administrators work through separate role-based interfaces. Founder accounts are created through a whitelist and verified with an email one-time password, and three service tiers (Standard, Elite, and Premium) control access to chat, document upload, and appointment-based calls. Founder ratings feed officer performance analytics. The system is built with Python, Flask, MySQL, and Scikit-learn. Its contribution is the integration of these parts into a single workflow, because the underlying algorithms are standard. The paper also defines the protocol for comparing the two classifiers using accuracy, precision, recall, and macro and weighted F1.

Dhanyaa Shree T, B. Meena Preethi, "Grievance AI: Startup Ecosystem Intelligence Hub" International Journal of Scientific Research and Engineering Development, V9(5): Page(1607-1615) September - October 2026. ISSN: 2581-7175. www.ijsred.com. Published by Scientific and Academic Research Publishing.