Teen Mood & Habit Compass for Schools & Counselors

Problem and target customer

Why this exists

Customer problem

Periodic surveys and scattered notes hide changes between counselor visits, but continuous monitoring can become unsafe if students lack consent controls or staff treat self-reports as diagnoses.

Who pays

School counseling teams serving secondary-school students with an approved wellbeing check-in program and defined escalation policy.

Business model

Annual school license priced by campus or enrolled program cohort, with onboarding and policy configuration included.

Editorial note

The defensible product is a consented self-report history with accountable human review. It should help a counselor notice change and organize follow-up without presenting an algorithm as a clinician.

A school must supply the operating policy the software enforces: who reviews, how quickly, what happens after a concerning response, and how withdrawal works. Without that staffing and governance, the interface creates a false promise of safety.

Choose your next step

What do you need next?

Evaluate the operating tradeoffs quickly, or inspect how to build the MVP.

Evaluate this ideaSee the cost and deployment preview

Compare the three operating paths at the default workload before opening the full calculator.

See cost preview Build this ideaInspect the build blueprint

Review MVP scope, workflow, states, data rules, architecture, and acceptance criteria.

Open blueprint

Evaluation preview

What would it take to run?

Directional infrastructure estimates for the current 3,000 student check-ins assumption. Open the full calculator when you are ready to change it.

Open full cost and deployment

ManagedSelected

$20-$65/ month

App, database, backups, and logs for private student check-ins and counselor review with minimal platform operations.

Lowest operating effort

Lean self-hosted

$7-$30/ month

A small cloud server runs private student check-ins and counselor review, PostgreSQL, and scheduled backups.

Lowest baseline cost

More control

$40-$160/ month

Separate web, data, and background capacity for private student check-ins and counselor review when operational isolation matters.

Most separation and control

Build blueprint

Build the first paid use case

Product goal

Who it serves and what it must change

Target user: Secondary-school students completing voluntary check-ins and counselors responsible for reviewing them.
Problem: Counselors lack timely, organized self-report history, while students need clear consent, privacy, and human accountability.
Measurable outcome: Every submitted check-in appears in the correct counselor queue, policy-triggered items receive a disposition within the school's window, and students can see whether participation is active.

MVP scope

What ships now and what waits

Included

Consent version and participation status
Mood and habit check-ins
Student history
Counselor review queue
Follow-up disposition
Retention and access audit

Excluded

Diagnosis
Automated crisis intervention
Parent access
Predictive scoring
Peer comparison

UX and user flow

Screens, actions, and states

Participation & Privacy

Explain the program, data use, limits, and current consent state.

Read policyConfirm assentPause participation

Daily Compass

Capture a small set of self-reported mood and routine signals.

Choose responsesAdd optional noteSubmit check-in

Counselor Review

Prioritize policy-triggered entries and document accountable follow-up.

Open student trendRecord contactSet disposition

Primary flow

Student confirms active participation
Student submits a check-in
Rules evaluate only configured thresholds
Counselor reviews context
Counselor records the response

Loading, empty, and error states

Participation: pending, active, paused, withdrawn
Check-in: submitted, queued, reviewed
Review: open, contacted, follow_up_planned, resolved, false_alarm

Core entity outline

Entities and business rules

ConsentRecord

Captures who agreed to which program policy and whether participation remains active.

Fields: student_id, policy_version, consent_source, effective_at, withdrawn_at
Relations: Belongs to Student, Governs CheckIns

CheckIn

Stores timestamped self-report answers and an optional protected note.

Fields: student_id, submitted_at, mood_value, habit_answers, note_ciphertext
Relations: Belongs to Student, May create ReviewItem

ReviewItem

Tracks the reason for counselor attention and the documented disposition.

Fields: check_in_id, rule_id, assigned_counselor_id, status, disposition, due_at
Relations: Belongs to CheckIn, Has many FollowUpNotes

Business rules

No check-in is accepted without active participation
Rules create review items but never a diagnosis
Only assigned counselors and approved supervisors can read protected notes
Withdrawal blocks new check-ins and follows the configured retention policy
Every disposition change records actor and time

Architecture and data flow

Components, integrations, and controls

Student portal

Present consent and accessible check-ins without exposing counselor notes.

Policy rules engine

Apply school-approved thresholds deterministically and explain why an item entered review.

Counselor workspace

Show trends, assignments, deadlines, and protected follow-up documentation.

Integrations

Optional school identity provider
Transactional reminders with no sensitive content

Data flow

Consent is checked before accepting answers
Encrypted check-in data is persisted
Rules create explainable review items
Counselor disposition closes or schedules follow-up

Failure handling

Save an offline-safe draft without marking it submitted
Keep a review item open when a reminder fails
Show staff a clear outage banner and preserve queue order after recovery

Security

Least-privilege counselor roles
Encryption in transit and at rest
No wellbeing text in analytics or email
Time-limited sessions and access audit

Rate limits

Limit repeated submissions per student
Throttle authentication attempts
Cap bulk exports and require supervisor authorization

Deliverables and acceptance

Definition of done for the MVP

Deliverables

Accessible student portal
Counselor queue and trend timeline
Consent and retention controls
Policy-rule configuration
Audit report
Threat model and recovery runbook

Acceptance criteria

A paused student cannot submit a check-in
A rule-triggered entry displays the exact rule and never a diagnosis
Unauthorized staff cannot open another counselor's notes
A failed reminder does not remove the item from review
Withdrawing participation follows the configured retention action

Recommended stack

Enough technology for the first version

Web application

Next.js and TypeScript

Supports accessible student forms and a separate counselor workspace.

Data

PostgreSQL with field-level encryption

Keeps consent versions, check-ins, and restricted notes connected with retention rules.

Authentication

School SSO or passwordless login

Avoids shared credentials and binds counselor access to approved roles.

Monitoring

Structured audit events

Records access and disposition changes without exposing check-in text in logs.

Why this is sufficient

This MVP is a privacy-sensitive reporting workflow, not an AI product. Deterministic policy rules, scoped access, and human review are easier to explain and test than predictive risk scoring.

Not required for the MVP

LLM analysisPredictive risk modelPaymentsMapsPublic student profiles

Next stepTurn the blueprint into an execution plan

Copy the build prompt, model the operating cost, and choose where to deploy.

Build and ship

Execution

Build, price, and deploy the MVP

Once the blueprint is clear, use the prompt, cost model, and deployment options to start building.

Build prompt

Copy this into a builder

Build a conventional TypeScript application with a relational database and explicit authorization; do not add model inference to the student or counselor path.

Build prompt

Your build prompt is ready

Open the prompt pack whenever you are ready to take this blueprint into your builder.

Based on the blueprintReady for your builderFollow-up steps included

Cost calculator

Model the MVP operating cost

A technical run-cost estimate for the first production version. Team, acquisition, payment fees, and business COGS are excluded.

Estimated monthly total$20-$65

$6.67-$22 per 1,000 student check-ins

Student check-ins / month3,000 student check-ins

Selected pathEasiest

Pricing checkedAug 2, 2026

Cost breakdown

$20-$65 per month

Low and high values allow for usage variance and plan headroom.

Managed Railway deployment

App, database, backups, and logs for private student check-ins and counselor review with minimal platform operations.

50K student check-ins included, then $8-$20 per 50K student check-ins

$20-$65

Included

Application and relational database hosting
Encrypted backups
Access audit events
Basic monitoring

Not included

Counselor staffing
Legal and safeguarding review
School SSO enterprise fees
Incident-response services
Student devices

Pricing basis

The estimate combines the selected infrastructure path, required operating modules, selected optional modules, and usage above included monthly allowances. Taxes and regional uplifts are excluded.

Railway pricing

Deployment options

Pick the operational tradeoff

Choose based on operating preference, not only the headline price.

EasiestRecommended

Railway

Deploy the student check-in and counselor review service with managed services, environment variables, logs, and a short path from prototype to beta.

$20-65/month at beta scale before workflow add-ons

Good fit

Fast deployment
Managed database
Simple previews

Limitation

Usage-based compute needs budget alerts as activity grows.

Open Railway

Cheapest

Vultr

Run the app, worker, and database on one small Vultr VPS with Docker Compose and explicit backups.

$7-30/month before workflow add-ons

Good fit

Low baseline
Portable Docker setup
Predictable capacity

Limitation

The operator owns database maintenance and incident response.

Deploy on Vultr View the lean VPS guide

More control

DigitalOcean

Separate application, worker, data, storage, and backup responsibilities as the workload grows.

$40-160/month before workflow add-ons

Good fit

Separated services
Capacity headroom
AWS ecosystem

Limitation

More moving parts increase setup and observability work.

Explore DigitalOcean View the separated cloud guide

Start building

Prototype, automate, then deploy

Recommended route

01Vibe-coding builderPrototype an age-appropriate check-in

Design the consent explanation, short check-in, counselor trend, and follow-up states with accessibility testing.

Open Lovable 02AutomationRelay operational reminders carefully

Use Make only for staff reminders that contain no student wellbeing details and link back to the protected queue.

Open Make 03DeployDeploy the protected workflow

Host the app and database with encrypted backups, strict secrets, audit logging, and a tested restore path.

Open Railway

Teen Mood & Habit Compass for Schools & Counselors

Is this worth validating?

Build this if

Avoid this if

Validate first

Why this exists

Customer problem

Who pays

Business model

What do you need next?

What would it take to run?

Build the first paid use case

Who it serves and what it must change

What ships now and what waits

Included

Excluded

Screens, actions, and states

Primary flow

Loading, empty, and error states

Entities and business rules

Business rules

Components, integrations, and controls

Integrations

Data flow

Failure handling

Security

Rate limits

Definition of done for the MVP

Deliverables

Acceptance criteria

Enough technology for the first version

Next.js and TypeScript

PostgreSQL with field-level encryption

School SSO or passwordless login

Structured audit events

Why this is sufficient

Not required for the MVP

Build, price, and deploy the MVP

Copy this into a builder

Your build prompt is ready

Model the MVP operating cost

Usage assumptions

$20-$65 per month

Included

Not included

Pricing basis

Pick the operational tradeoff

Railway

Good fit

Limitation

Vultr

Good fit

Limitation

DigitalOcean

Good fit

Limitation

Prototype, automate, then deploy

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