Before & After
- Mental health assessment took 1 hour per student, done manually by counselors
- District-wide screening was practically impossible — too slow, too costly
- At-risk students were being missed because there was no way to screen everyone
- No centralized data — counselors had no visibility into district-wide trends
- Counselors spent most of their time on process instead of actual student support
- 6,000 students processed in just over one week
- AI-powered risk analysis identifies students that manual review missed
- Counselors focus their time on intervention, not paperwork
- Google Looker dashboards give administrators real-time district-wide visibility
- Automated survey distribution runs without adding staff workload
- Secure, FERPA-compliant platform deployed across the Southeastern US
The Challenge
Mental health is one of the biggest issues facing students today. But identifying which students need help is hard. You cannot just look at a student and know. And there are thousands of them.
The traditional approach was simple: a counselor sits down with a student for about an hour, asks questions, and writes up an assessment. That works well for one student. It falls apart completely when you have thousands. A district trying to screen everyone the old way would take months — or years. Most districts never even tried. At-risk students stayed invisible until something went wrong.
Fidelity Healthcare Group serves students across the Southeastern United States and knew the problem was bigger than any counselor team could handle alone. They needed a way to screen a large student population quickly, securely, and accurately — without replacing the human judgment of trained counselors. The AI had to help, not take over.
The challenge was not just speed. The data had to stay private. The surveys had to reach students across different schools and devices. The results had to be easy for counselors to act on. And the whole system had to be reliable enough to trust when a student's wellbeing was on the line.
That is exactly the problem we set out to solve — a platform that could scale screening to every student, surface the ones who needed attention, and put counselors back where they belong: having real conversations and making a real difference.
What We Built
How did we make mass screening actually possible?
We built an automated survey distribution system that sends assessments to students across the district without requiring counselors to manually reach out to each one. Students complete the survey on any device — phone, tablet, or desktop. The platform handles distribution, reminders, and response collection automatically, turning what used to be a one-on-one time sink into a scalable, district-wide process. That is how 6,000 students got assessed in just over a week instead of months.
How does the AI figure out which students are at risk?
The AI analyzes student responses against validated clinical frameworks for depression and mental health risk. It looks at combinations of signals — sleep patterns, mood, social connection, concentration, and more — that individually might seem small but together paint a clearer picture. It surfaces students whose response patterns match early indicators of concern and places them in a prioritized counselor review queue. The AI never diagnoses — it flags, and trained humans decide what happens next. This combination caught students that pure manual review had been missing.
What did the frontend experience look like for students and educators?
We built the frontend in React and Next.js, designed alongside educators and students to make it feel approachable rather than clinical. Students see a clean, straightforward survey experience that does not feel intimidating. Counselors get a clear dashboard showing flagged students, risk levels, and the specific signals that triggered each flag. Administrators see Google Looker analytics dashboards with district-wide trends — which schools have higher rates of concern, how response rates track over time, and where to focus resources next.
What technology powered the backend and notifications?
The backend is built in C# and hosted on Microsoft Azure, giving us enterprise-grade reliability and security. Azure SQL Database stores all student data with encryption at rest, and the system uses role-based access controls so only authorized staff see sensitive information. Amazon SES handles automated email notifications — alerting counselors when a student is flagged, and sending survey reminders to students who have not yet responded. The entire stack was chosen for compliance, performance, and the ability to scale across a large district without degrading.
How did we protect student privacy?
Student privacy was a non-negotiable from day one. The platform is FERPA-compliant and built on Azure's secure infrastructure. All data is encrypted in transit using TLS and at rest using AES-256. Access is strictly role-based — a teacher cannot see a student's mental health responses, and a counselor at one school cannot access students from another. No student data is used to train AI models. Every design decision was reviewed against FERPA requirements before going into production.
Fidelity Healthcare Group — AI Wellness Check Platform
The Results
"NetAesthetics transformed our mental health assessment process from a 1-hour-per-student bottleneck into a secure, district-wide system that processed 6,000 students in just over a week. Their AI-powered risk analysis helps our counselors identify at-risk students we might have missed with manual review. The combination of speed, security, and accuracy has been game-changing for student safety."Fidelity Healthcare Group — Southeastern United States
Project Details
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