Behind the Build: How We Created an AI-Powered Drone Pilot Assessment & Feedback Platform

Introduction

In the rapidly evolving world of drone technology, both aspiring and experienced pilots need smarter, more interactive tools to assess their readiness, receive personalized training recommendations, and interact with AI-driven systems. Our project answers this need—a modular, AI-powered platform designed to streamline drone pilot assessment, feedback, and real-time interaction.

Built for drone training organizations, tech startups, and individual enthusiasts, this platform leverages state-of-the-art AI to deliver instant, actionable insights and a seamless user experience. What sets it apart is its deep integration of OpenAI’s language models, real-time feedback, and a robust, scalable architecture.

General Overview

At its core, the platform enables users to:

  • Assess their drone pilot readiness through a conversational AI interface.
  • Receive personalized training module recommendations based on their experience and goals.
  • Interact with a real-time console for chat, feedback, and drone-related queries.
  • Get instant, AI-powered feedback on their responses and progress.

Key Features:

  • Conversational AI chatbot for assessment and Q&A
  • Automated extraction of user profile and experience
  • Dynamic training module recommendations
  • Real-time feedback and progress tracking
  • Modern, responsive web interface

Tech Stack

Frontend:
- React (TypeScript) - React Router for navigation - Leaflet for interactive maps - Three.js for 3D visualization - OpenAI Realtime API (browser client) - Sass for styling

Backend:
- NestJS (Node.js, TypeScript) - Prisma ORM (database abstraction) - Axios for HTTP requests - Swagger for API documentation - Class-validator for input validation

AI Backend:
- FastAPI (Python) - Uvicorn (ASGI server) - OpenAI Python SDK - Custom prompt engineering

Database:
- Managed via Prisma (commonly PostgreSQL or MySQL)

Infrastructure / DevOps:
- Poetry for Python dependency management - npm for Node/React - Modular microservices (potential for Docker/Kubernetes)

AI/ML Tools:
- OpenAI GPT models (via API) - Custom prompt templates and response parsing

How AI Powers the System

AI is the heart of this platform, driving the assessment, feedback, and recommendation engines.

AI’s Role: - Natural Language Understanding: Extracts user experience, goals, and knowledge from free-form text. - Personalized Recommendations: Suggests training modules tailored to each user’s background. - Conversational Agent: Engages users in dynamic, context-aware dialogue. - Profile Extraction: Summarizes user data for backend storage and analytics.

Models Used: - OpenAI GPT (e.g., GPT-4) for all NLP tasks.

Integration Method: - The AI backend (FastAPI) communicates with OpenAI via the official Python SDK. - Prompts are carefully engineered and dynamically filled with user input. - Responses are parsed, validated, and returned to the main backend and frontend.

AI Tasks: - Information extraction (from user input) - Question answering - Module recommendation - Profile summarization

Challenges & Solutions: - Parsing AI Output: Custom logic ensures robust extraction of structured data from GPT’s responses, even when output is not perfectly formatted. - Prompt Engineering: Iterative refinement of prompts to maximize accuracy and relevance. - Error Handling: Decorators and retries for resilient API calls.

Technical Breakdown

Database Structure: - User records, chat logs, assessment results, and module recommendations are stored via Prisma ORM. - Flexible schema supports rapid iteration and new features.

Backend Design: - NestJS serves as the main orchestrator, exposing RESTful APIs for chat, assessment, and recommendations. - FastAPI microservice handles all AI-related tasks, keeping AI logic isolated and scalable. - Relay server (Node.js) manages real-time communication and API key security.

Key API Routes: - /bot/drone-assessment (POST): Triggers AI-driven assessment and information extraction. - /chat: Retrieves chat history for a user. - /module-recommendation: Returns personalized training modules.

Engineering Decisions: - Microservices: Decouples AI from business logic for scalability and maintainability. - Async Operations: Both Python and Node backends use async patterns for high throughput. - Global Middleware: Handles CORS, validation, and error management.

User Journey Walkthrough

  1. Landing on the Platform
  2. User visits the web console (React app).

  3. Presented with options: start a chatbot session or view feedback.

  4. Starting an Assessment

  5. User initiates a chat with the AI.

  6. The frontend sends user input to the backend API.

  7. Backend Processing

  8. The main backend receives the request and forwards it to the AI backend.
  9. The AI backend crafts a prompt and calls the OpenAI API.

  10. AI extracts user experience, goals, and knowledge from the input.

  11. AI-Driven Recommendation

  12. AI backend returns structured data (profile, assessment, recommendations).

  13. Backend stores results and sends them to the frontend.

  14. User Receives Feedback

  15. The frontend displays personalized feedback and recommended training modules.

  16. User can review their assessment transcript and explore suggested courses.

  17. Real-Time Interaction

  18. Users can continue chatting, ask questions, or provide feedback.
  19. All interactions are logged and analyzed for continuous improvement.

Text-Based Flowchart / Architecture

User
  ↓
Frontend (React App)
  → User enters input (chat, assessment, feedback)
  ↓
Backend API (NestJS)
  → Receives request
  → Validates and logs input
  ↓
AI Backend (FastAPI)
  → Receives request from main backend
  → Crafts prompt for OpenAI GPT
  → Calls OpenAI API
  → Parses and validates AI response
  ↓
Backend API (NestJS)
  → Stores results in database (Prisma)
  → Returns structured response to frontend
  ↓
Frontend (React App)
  → Displays feedback, recommendations, and chat history
  → User continues interaction or explores further

Conclusion

This project demonstrates the power of combining modern web technologies with advanced AI to deliver a seamless, intelligent user experience. By leveraging microservices, robust backend frameworks, and state-of-the-art language models, we’ve built a platform that’s scalable, maintainable, and truly impactful for drone pilot training and assessment.

Our team’s expertise in AI integration, prompt engineering, and full-stack development shines through in every aspect of the system. Looking ahead, we plan to expand the platform with more AI-driven features, deeper analytics, and broader integrations—empowering users and organizations to reach new heights in drone technology.