Nimbi is a data‑driven student success platform for higher‑education institutions that uses custom predictive models to identify at‑risk students and automatically generate actionable tasks for advisors and support staff. By integrating with existing LMS, CRM, and SIS systems, it provides real‑time risk scores, intervention dashboards, and coordinated outreach recommendations to improve engagement, retention, and graduation outcomes.
Funding
Funding not disclosed
Founders
Product
Problem
Higher education institutions struggle to identify at‑risk students early and to coordinate timely interventions, leading to low engagement, high dropout rates, and suboptimal graduation outcomes.
Solution
Nimbi provides a data‑driven student success platform that integrates advanced analytics and custom machine‑learning models into daily workflows of academic support teams. The system ingests historical and real‑time student data, predicts risks such as dropout, low attendance, or poor performance, and automatically generates actionable tasks for advisors, tutors, and support staff. These tasks include personalized outreach recommendations, enrollment or re‑enrollment prompts, and targeted retention strategies. Results and impact metrics are tracked in a unified dashboard, enabling institutions to measure intervention effectiveness and continuously improve student outcomes.
Target Audience
Primary customers are universities, technical institutes, and e‑learning providers that need to improve student retention, engagement, and graduation rates through predictive analytics and coordinated support workflows.
Features
- Custom predictive models built from each institution’s historical data, covering metrics like dropout probability, enrollment likelihood, and academic performance
- Automated task generation that assigns specific actions to support staff with context, timing, and rationale
- Integrated analytics dashboard showing real‑time student risk scores, intervention impact, and key success indicators
- Seamless integration with existing LMS, CRM, and student information systems via APIs and pre‑built connectors (e.g., Blackboard Ultra, WhatsApp)
- Continuous model updates and software releases with minimal disruption, delivering new features several times per year
- Configurable data ingestion supporting demographic, socioeconomic, and behavioral data sources