Welcome to the Otonoco AI coding challenges!

These challenges are designed to cover a wide range of topics and will test your programming skills. These programming challenges cover for Web, Backend and/or AI development.

This challenge combines AI integration with modern frontend development, testing both technical skills and user experience design capabilities.

Challenge: AI-Enhanced Regulatory Document Explorer

Build a simple regulatory document discovery app that demonstrates AI integration with frontend development. This is designed to showcase your approach to problem-solving, architecture decisions, and implementation skills within a limited timeframe.

1. Basic Regulatory Interface

   • Fetch regulatory data from a public API (e.g., SEC Edgar API, FDA API, or FCC API)
   • Display document list with basic information (title, date, agency, document type)
   • Create a simple detail view for selected documents
   • Basic responsive design

2. One AI Feature (Choose One)

   • AI-powered Search: Natural language search using any AI service
     • Example: "show me recent FDA drug approvals" or "find banking regulations about cryptocurrency"
   • AI Document Analysis: Generate compliance summaries or key points from regulatory text
   • AI Content Summary: Use AI to create concise summaries of complex regulatory documents

3. Basic User Features

   • Simple bookmarking system with local storage for important documents
   • Basic filtering (by agency, date, or document type)
   • Loading states and error handling

4. Technical Implementation

   • Choose one frontend framework (React, Vue, or vanilla JS)
   • Integrate with one AI service (Gemini, OpenAI, Anthropic, or Hugging Face)
   • Basic state management (can be simple useState/reactive data)
   • Clean code structure with comments explaining your approach

• Problem-solving approach: How you break down the requirements
• Architecture decisions: Your choice of tools and structure
• AI integration strategy: How you handle API calls, errors, and user experience
• Code quality: Clean, readable code with clear intent
• Trade-off awareness: Understanding of what you prioritized and why

• Working application (hosted link or local setup instructions)
• Brief README explaining:
  • Your approach and architecture decisions
  • Which features you chose to implement and why
  • Any challenges faced and how you solved them
  • What you would improve with more time
• Simple, clean codebase with key decisions documented

• Add compliance risk scoring or document categorization
• Implement document comparison features
• Add simple data visualization (charts for document trends)
• Include regulatory change tracking or alerts

• Upload your code to GitHub/GitLab.
• Include a README.md with:
• Instructions to run the app
• Any known limitations or trade-offs
• Your thought process and architecture decisions
• Documentation of AI integration and regulatory data handling approach

4–6 hours total. Focus on demonstrating your thought process and technical decision-making rather than completing every possible feature. We're more interested in seeing how you approach problems and structure solutions than in a fully-featured application.

This challenge is designed to give us insight into:

• How you approach technical problems and make architectural decisions
• Your understanding of AI integration patterns and user experience considerations
• Code organization and documentation skills
• Ability to prioritize features and manage scope within time constraints

The goal is to have a meaningful technical discussion about your implementation choices, trade-offs, and potential improvements.