Jennie AI is a SaaS platform that automates language school operations via WhatsApp, handling class confirmations, reminders, rescheduling and material distribution while reducing manual work by up to 80%.
Funding
Funding not disclosed
Founders
Product
Problem
Language schools often rely on manual processes for class confirmations, reminders, rescheduling, and student engagement, leading to high administrative workload, missed classes, and student attrition.
Solution
Jennie AI offers a SaaS platform that automates routine school operations through WhatsApp, handling class confirmations, reminders, and rescheduling without human intervention. The system continuously monitors attendance, engagement, and performance data to generate predictive alerts that identify students at risk of dropping out, enabling proactive interventions that can recover up to 40% of at‑risk learners. Integrated 24/7 AI tutoring provides personalized, on‑demand support, while analytics surface curriculum weaknesses and operational bottlenecks, allowing schools to optimize teaching plans and improve overall efficiency.
Target Audience
Jennie AI is designed for language school administrators, teachers, and operations managers who need to streamline administrative tasks, improve student retention, and enhance pedagogical outcomes.
Features
- Automated WhatsApp messaging for class confirmations, reminders, material distribution, and rescheduling with up to 80% reduction in manual work
- Predictive churn analytics that assess attendance, engagement, and performance to flag at‑risk students and suggest preventive actions
- 24/7 AI‑driven tutoring that answers student queries in real time, referencing specific lesson content
- Insight engine that identifies difficult concepts, tracks curriculum effectiveness, and recommends content adjustments
- Centralized dashboard integrating with Zoom, Meet, and Teams, offering natural‑language queries and instant reporting
- High open‑rate messaging (≈90%) ensuring students stay informed and prepared for each session