Web App Calculating Customers VQ Score

Role: Lead Full-stack Developer & UX/UI Adviser Stack: Vue.js, Node.js, Typescript, Tailwind, PostgreSQL, SQL, Monorepo, DigitalOcean, Figma, Datadog, Retool

Overview

Developed the MVP version of VQ, a web application used for calculating and presenting a "Value Quotient" (VQ) Score for users based on guided inputs and behavioral data. The goal of the project was to deliver a fast-to-iterate MVP with high code quality, strong observability, and a UX that guided users smoothly through the scoring process.

The product needed to validate core assumptions early, so rapid prototyping + real usage feedback were central to the development approach. I led the technical implementation and also provided UI/UX guidance to refine flows, reduce friction, and align the scoring experience with brand tone.

Responsibilities & Contributions

• Iterative Agile Development: Built the product using a fast feedback loop where the client could continuously test the app and evolve product ideas collaboratively.
• End-to-End Type Safety: Implemented a fully typed monorepo architecture — from PostgreSQL schema to backend services to the Vue frontend — ensuring reliability and maintainability.
• Observability & Usage Analytics: Set up Datadog Real User Monitoring (RUM) to track:
  • Frontend performance & errors
  • Backend logs & resource usage
  • User behavior and navigation flows This allowed the client to make informed product decisions based on real user interaction data.
• Testing & CI/CD: Added integration tests and wired them into the CI/CD pipeline to ensure quality and reduce regression issues across deployments.
• Admin & Internal Tooling: Implemented a Retool-based admin dashboard to provide instant visibility into:
  • User activity
  • Score distributions
  • Operational & usage metrics This was delivered in under 2 hours of engineering, maximizing leverage and reducing development overhead.

Impact

• Delivered a functional, testable MVP quickly, allowing the client to validate product assumptions with real users.
• Improved product decision-making through high observability and data-driven insights rather than guesswork.
• Created a scalable codebase with strong correctness guarantees thanks to end-to-end typing.
• Enabled the client to operate the product and review key business metrics with zero ongoing developer dependency through the Retool dashboard.

Screenshots of the app

You may try it out yourself in the project link, or here are some attached screenshots: