Screenshot

About SurveyServe

Why I Built This

SurveyServe came out of a problem I saw firsthand: researchers spending weeks, sometimes months, trying to implement validated psychological surveys like the PHQ-9 or GAD-7 into digital tools. The process was slow, expensive, and frustrating. Most existing platforms were either built for general-purpose surveys (like customer feedback) or priced far beyond what research budgets could afford.

I wanted to fix that with a tool that could save researchers thousands of dollars and hours by making validated instruments available, usable, and easy to score without compromising on the scientific integrity that makes them valuable in the first place.

What I Learned

Technical Skills

Built the platform using Bolt.new with: React, TypeScript, Supabase, and Stripe
Designed a flexible scoring engine to handle everything from raw scores to complex thresholds
Integrated secure payment with Stripe, including credit-based and freemium pricing models
Added privacy and consent management in line with research ethics

Domain Knowledge

Studied the structure and scoring of validated psychological assessments
Gained insight into psychometric properties and academic publishing norms
Built workflows and interfaces aligned with real research practices

Business & Product Strategy

Validated the idea through conversations with real researchers
Balanced academic accessibility with a sustainable pricing model
Learned how to design around non-technical users in high-stakes, detail-sensitive environments

How the System Works

SurveyServe runs on a modern, scalable stack:

Frontend: React + TypeScript, Tailwind CSS, React Router
Backend: Supabase for auth, database, and real-time data
Payments: Stripe for credit-based and pay-per-survey billing
Hosting: Netlify with automatic CI/CD

The platform includes a scoring engine capable of handling:

const scoringRules = {
  depression_score: {
    type: "sum",
    questions: ["q3", "q5", "q11", "q14", "q16", "q19", "q20"]
  },
  depression_severity: {
    type: "threshold",
    input: "depression_score",
    thresholds: [
      { min: 0, max: 9, label: "Normal" },
      { min: 10, max: 13, label: "Mild" },
      { min: 14, max: 20, label: "Moderate" }
    ]
  }
}

This engine supports reverse scoring, subscales, conditional logic, and clinical thresholding, all in a declarative format researchers can understand.

Key Features

Instrument Library: Validated surveys across mental health, well-being, personality, and workplace stress
Real-Time Analytics: Live response monitoring, automatic scoring, and trend summaries
Compliance: Anonymous data collection, and consent management
Flexible Schema: Surveys stored as JSONB for schema flexibility without losing query performance
Export Tools: CSV and JSON downloads for research use

Challenges I Faced

Technical

Building a generalized scoring engine that doesn’t require code changes per instrument
Designing a flexible database schema to support conditional and multi-part surveys
Creating secure, resilient billing flows with custom webhook logic

Domain

Navigating licensing rules for each instrument (attribution, public domain, commercial)
Avoiding scoring errors with real clinical implications
Making the UX usable for researchers with limited technical skills

Business

Pricing for researchers while covering platform costs
Getting early validation from the academic community
Supporting both casual users and enterprise-scale research orgs

Highlights & Impact

Peer-reviewed psychological instruments ready to use
Built-in scoring and analytics with real-time updates
Research-grade security and privacy workflows
Academic-friendly pricing with a freemium model
Designed for mobile and desktop use
Easy data export for analysis or publication

Where It’s Going

SurveyServe is just the start. Future plans include:

Expanding the instrument library to cover more domains
Adding AI-powered scoring insights and pattern detection
Supporting team-based research and collaboration features
Offering deeper integration with research platforms and APIs

This project started as a fast, focused build to solve a clear, high-friction problem. But it’s grown into something that could seriously improve how research gets done. Making rigorous tools accessible shouldn't be hard—and now, it isn’t.