Back to work

Mobile + Backend

MindMitra

Cognitive-wellness app (iOS + Android)

MindMitra (formerly CognizenX) is a cognitive-wellness app built with DePauw Neuroscience. I led a three-engineer team decoupling AI and data workflows from the shipped React Native client onto a dedicated Vercel/MongoDB backend—isolating ML updates from mobile release cycles while delivering iOS and Android builds with telemetry-driven spaced-repetition scheduling.

Role
Software Engineer – MindMitra
Team size
3 engineers
Timeframe
Aug 2025 – May 2026
Status
Shipped v2 (iOS + Android)
Links

Previously released as CognizenX on the App Store; the product is now branded MindMitra.

Product screenshots

Context and problem

A shipped React Native cognitive-wellness app needed a backend-first architecture, Android support, server-bound AI generation, and quiz telemetry for research-informed spaced repetition.

Challenge

Take over a live v1 product under its prior CognizenX branding, decouple AI from the mobile client, expand to Android, and instrument telemetry for spaced-repetition research workflows without disrupting existing users.

Constraints

  • Existing users and shipped iOS codebase to refactor without breaking core quiz flows
  • Early deployment scope—not a large-scale production rollout
  • Cross-platform release differences between iOS (TestFlight) and Android (Play internal track)
  • App Store listing still published under the prior CognizenX name during the MindMitra rebrand

Key contributions

  • Led a 3-engineer team to migrate AI and data workflows from a shipped React Native app into a dedicated backend architecture, enabling independent backend releases and faster iteration on ML-powered features.
  • Built and maintained a 25-endpoint Node.js/Express backend on Vercel and MongoDB Atlas to centralize authentication, AI content generation, and scheduling workflows behind a unified API layer.
  • Developed an automated GPT-4 content pipeline that generated 3,000+ questions across 40+ subdomains and ran ~300,000 embedding-based similarity checks to enforce semantic deduplication.
  • Improved backend security and maintainability by moving exposed AI credentials server-side, implementing rate limiting and request validation, and expanding test coverage to 65 automated tests (~60% coverage).

Architecture

Key engineering decisions

  • Migrated AI and data workflows onto a dedicated backend to enable independent releases and faster ML iteration
  • Moved exposed AI credentials server-side with rate limiting and request validation on backend routes
  • Invested in embedding-based deduplication and automated test coverage (~60% backend) before scaling content generation

Security and reliability

  • Embedding-based semantic deduplication across ~300,000 similarity checks to reduce redundant GPT-4 calls
  • Rate limiting, request validation, and session authentication on backend routes
  • 65 automated tests achieving ~60% backend code coverage

Testing

  • 65 automated tests with ~60% backend code coverage

Outcome

Delivered MindMitra v2 on iOS and Android with a decoupled backend, 25-endpoint API, GPT-4 content pipeline (3,000+ questions), and telemetry-driven spaced-repetition scheduling.

Limitations

  • Early deployment—not a large-scale production rollout
  • iOS App Store listing remains under the prior CognizenX URL during rebrand

Next steps

  • Complete MindMitra rebrand across app store listings
  • Expand per-question progress tracking for spaced-repetition analytics

Tech stack

  • React Native
  • Node.js
  • Express
  • MongoDB Atlas
  • GPT-4
  • Vercel
  • Jest
  • Supertest