ITNeuroNet
TodaiHealth

Health-tech platform delivery with detailed architecture, documentation, and contract-first planning

TodaiHealth shows how detailed product and technical documentation can help AI-supported implementation move faster. The work included health-tech domain modeling, architecture design, API contracts, data models, and a large implementation plan built around microservices.

Discuss a Health-Tech MVP Book a Discovery Call
Why it mattered

This project needed more than fast coding. It required a clear architecture, defined domain boundaries, API contracts, data models, and enough documentation to keep AI-supported implementation aligned with the intended design.

What ITNeuroNet did

The work started with specifications, architecture, and product documentation. Then came contract-driven implementation, followed by stabilization through tests, integration checks, and frontend development.

Architecture / delivery pattern

The patterns that shaped this system

  • 01

    Specification-first delivery

  • 02

    Microservice decomposition

  • 03

    Contract-driven implementation

  • 04

    AI-assisted implementation under senior architecture review

Public scale signals

What the system looks like from the outside

  • 18 microservices delivered after the specification phase
  • 49 OpenAPI specifications
  • 615 API operations
  • 652 data schemas
  • 147 database tables
  • 1,200+ Markdown files in the documentation
  • 436,000+ lines of documentation
What it demonstrates

This project shows careful healthcare-oriented architecture, detailed documentation, contract-first planning, a microservice split, and faster AI-assisted implementation after a thorough specification phase.

This case shows that solid architecture, API planning, data modeling, and good documentation can make AI-supported implementation faster and easier to control.

Relevance to future clients

Useful for founders and health-tech teams that need to validate, plan, or build a complex MVP without losing architectural discipline.

What this made faster
  • 01Time from documented scope to first reviewable architecture
  • 02Bringing new contributors in through the documentation layer
  • 03AI-assisted implementation cycles against fixed contracts
Quality / safety controls
  • 01Contract-first APIs with 49 OpenAPI specifications
  • 02Documented architecture decisions reviewed change by change
  • 03Test-aware delivery under senior architecture review
Related trendAI-native time to market
Relevant services

Engagements where this case applies

Public safety note

Sensitive implementation details are omitted from the public version. More detailed technical discussions may be possible during discovery where appropriate.

Next step

Discuss a Health-Tech MVP?

Share your situation. I will review it and suggest the most practical next step: an assessment, a discovery call, or a more specific engagement.

Discuss your projectBook a Discovery Call