Santulan

The description states that Santulan is a privacy-first burnout detection system for exam-prep students, combining passive laptop study signals with Android recovery signals to generate a daily Burnout Risk Score from 0 to 100.

It consists of:

• A Windows agent that captures aggregate study behavior without recording screenshots, keystrokes, or private messages.
• An Android app collecting recovery signals like first unlock time and usage patterns with consent.
• A backend and ML pipeline that processes metrics, builds personal baselines, detects anomalies, and produces a burnout risk score.

The MVP uses simulated student personas and real-device testing to validate the end-to-end flow. It employs baseline comparison, anomaly detection, weighted fusion of PC and phone signals, and trend logic for alerts.

Confidence Low — this is self-reported functionality with no independent verification or demonstration beyond the hackathon submission.

The description states that Santulan was inspired by the pressure faced by JEE and NEET students in India, where burnout builds silently over weeks before being noticed. The system aims to provide a silent safety net for parents, helping them detect risk early without relying on self-reporting from students.

It positions itself as:

• A privacy-first solution, avoiding invasive monitoring.
• Focused on early detection, not diagnosis or intervention.
• Designed around the real lives of Indian exam-prep students, including long study hours and family involvement.

Inference The positioning implies a shift from reactive mental health support to proactive early warning, but no evidence exists that this approach has been validated with users or tested in real-world settings.

The description states that Santulan is built for exam-prep students in India, specifically those preparing for JEE and NEET exams. It targets:

• Parents who want to monitor their children’s well-being.
• Families involved in high-pressure educational journeys.

It also mentions that the system is shaped around the real lives of Indian exam-prep students, including long laptop study hours, family involvement, and the need for support before a crisis happens.

Confidence Low — no evidence of actual customer interviews, user feedback, or market validation beyond the hackathon context.

The description does not provide any information about:

• Revenue model
• Pricing structure
• Monetization strategy
• Customer acquisition plans

It only describes a demoable MVP for a hackathon and mentions future exploration of coaching institutes, counselors, and families as potential users.

Confidence Not evidenced — no commercial or pricing data available.

The system is described as:

• A cross-device system with three main parts: Windows agent, Android app, and backend.
• Built using technologies including React, Python, TypeScript, scikit-learn, pandas, OpenAI’s GPT-5, and data visualization tools.
• Designed to process signals such as study vs. distraction time, idle time, focus switching, first phone unlock time, and phone activity categories.
• Uses baseline comparison, anomaly detection, weighted fusion of PC and phone signals, and trend logic for alerts.

It is noted that the MVP was built using simulated student personas and real-device testing to validate the end-to-end flow.

Confidence Medium — technical architecture is described in detail but lacks evidence of deployment or scalability beyond a hackathon prototype.

The description states:

• This is an MVP built for a hackathon.
• It uses simulated student personas and real-device testing to validate the end-to-end flow.
• The team has not yet launched or scaled beyond this prototype.

There is no evidence of:

• Actual users
• Customer feedback
• Revenue
• Product adoption
• Market traction

Confidence Very low — no signs of real-world usage or product maturity beyond a hackathon demo.

The description does not mention any competitors or existing solutions in the mental health or student wellness space. It only states that Santulan is designed to be privacy-first, avoiding invasive monitoring, and focused on early detection rather than diagnosis.

Confidence Not evidenced — no competitive analysis or market positioning beyond self-description.

• Privacy vs. Utility Trade-off: The system claims to avoid invasive data collection but must balance this with meaningful signal detection.
• Lack of Real-World Testing: No evidence that the system has been tested with actual students or parents.
• Unproven Model Reliability: The model is described as using baseline comparison and anomaly detection, but no performance metrics or validation results are provided.
• No Commercial Viability: No indication of how the product would be monetized or scaled beyond a hackathon prototype.
• Ethical Concerns: Early burnout detection systems may raise ethical questions around surveillance and consent, especially in educational settings.

Confidence Medium — risks are inferred from the lack of evidence and the nature of the problem domain.

1. What were the specific outcomes of real-device testing with actual students or parents?
2. How was the model validated? Were there any performance benchmarks or accuracy metrics shared?
3. Has the system been tested in a real educational environment (e.g., coaching institutes)?
4. What is the plan for scaling beyond the MVP and ensuring long-term privacy compliance?
5. How do you intend to build trust with students, parents, and institutions around data usage?
6. Are there any partnerships or pilot programs already underway?