Behind the Build: How We Created Mind Orbi — An AI-Powered Multi-Platform Experience

Introduction

Mind Orbi is a cutting-edge, AI-driven platform designed to revolutionize how users interact with intelligent systems across web, mobile, and browser extension interfaces. Built for forward-thinking businesses and users who demand seamless, insightful digital experiences, Mind Orbi combines advanced AI, robust backend engineering, and modern frontend design. The platform solves the challenge of delivering real-time, context-aware AI services—like chat, job search, and content analysis—across multiple touchpoints, all while maintaining security, scalability, and ease of use. What sets Mind Orbi apart is its modular AI backend, deep integration with cloud and DevOps tools, and a user journey that feels both powerful and intuitive.

General Overview

At its core, Mind Orbi is a multi-component system that empowers users to:

Chat with AI for support, content generation, or analysis
Search and analyze job listings using a custom scraper
Access features via web, mobile, or browser extension
Benefit from real-time AI-powered insights (NLP, voice/text analysis, summarization, and more)

Key features include: - AI chat and content review - Job data aggregation and analysis - Multi-platform access (web, mobile, extension) - Secure, scalable cloud-native deployment

Tech Stack

Frontend:
- Next.js (React, TypeScript) for web and landing page
- Expo (React Native) for mobile
- Vite + React for browser extension

Backend:
- NestJS (Node.js, TypeScript) REST API
- Fastify for high-performance HTTP handling
- Prisma ORM for database access
- Redis for caching and queues
- Sentry for error monitoring

Database:
- PostgreSQL (via Prisma ORM)

Infrastructure / DevOps:
- Docker & Docker Compose for local development
- Helm & Kubernetes for cloud deployment
- GCloud Registry, S3 for storage
- Makefile for build automation

AI/ML Tools and Frameworks:
- FastAPI (Python) for AI microservices
- HuggingFace Transformers for NLP
- OpenAI API for advanced language models
- Custom fine-tuned models
- Uvicorn for ASGI server

How AI Powers the System

AI is the heart of Mind Orbi, enabling features that go far beyond simple automation:

Role: AI handles chat, content analysis, language detection, voice/text analysis, and job data enrichment.
Models Used:
HuggingFace models for NLP tasks
OpenAI (e.g., GPT-4) for advanced language understanding
Custom fine-tuned models for domain-specific tasks
Integration:
AI services are exposed via FastAPI endpoints
The backend communicates with AI via REST APIs
Models are loaded and managed in a modular, scalable way
AI Tasks:
Natural Language Processing (NLP)
Classification and summarization
Language and voice detection
Real-time chat and content review
Challenges & Solutions:
Challenge: Managing large models and fast inference
Solution: Local caching, HuggingFace cache management, and efficient API design
Challenge: Secure, scalable deployment
Solution: Containerization, cloud storage, and robust API authentication

Technical Breakdown

Database:
PostgreSQL, managed via Prisma ORM
Stores user data, chat history, job listings, and AI analysis results
Backend Architecture:
Modular NestJS app with feature-based modules (auth, users, jobs, AI, etc.)
RESTful APIs, with Swagger and Redoc for documentation
Redis for caching and background jobs (BullMQ)
AI Service:
FastAPI app, modular endpoints for each AI task
HuggingFace and OpenAI integration
Handles SSL, environment config, and model caching
Engineering Decisions:
Microservices for scalability
Docker/Helm for reproducible deployments
Type safety and code quality via TypeScript and ESLint
Key API Routes:
/api/chat — AI chat endpoint
/api/jobs — Job search and analysis
/api/analysis — Content and voice/text analysis
/api/auth — Authentication and user management

User Journey Walkthrough

User visits the web app, mobile app, or opens the browser extension.
They log in or sign up, then access the main dashboard.
User initiates an action (e.g., starts a chat, searches for jobs, uploads content for analysis).
Frontend sends a request to the backend API.
Backend authenticates the user and routes the request:
For chat or analysis, backend calls the AI FastAPI service.
For job search, backend may trigger the job scraper microservice.
AI service processes the request:
Loads the appropriate model (HuggingFace, OpenAI, or custom)
Runs NLP, classification, summarization, or analysis
Stores results in the database if needed
Backend receives AI results, applies any business logic, and returns the response to the frontend.
User sees the result in real time—AI-generated content, analysis, or job insights.
User can interact further, with each action triggering the same seamless flow.

Text-Based Flowchart / Architecture

User (Web/Mobile/Extension)
    ↓
Frontend (Next.js / React Native / Vite)
    ↓
API Request (REST)
    ↓
Backend (NestJS)
    → Auth & Validation
    → Business Logic
    → Calls AI Service (FastAPI) or Job Scraper (Python)
        ↓
    AI Service (FastAPI)
        → Loads Model (HuggingFace/OpenAI/Custom)
        → Runs AI Task (NLP, Analysis, etc.)
        → Returns Result
    Job Scraper (Python)
        → Scrapes Job Data
        → Returns Data
    ↓
Backend
    → Stores/Retrieves Data (PostgreSQL via Prisma)
    → Caches Results (Redis)
    ↓
Frontend
    → Displays Results to User

Conclusion

Mind Orbi is a showcase of what’s possible when modern engineering meets advanced AI. The project demonstrates our team’s ability to architect scalable, secure, and user-friendly systems that leverage the latest in AI and cloud technology. From seamless multi-platform access to real-time, AI-powered insights, Mind Orbi is both a technical achievement and a user experience triumph. Looking ahead, we plan to expand the platform’s AI capabilities, add more integrations, and continue pushing the boundaries of what intelligent digital experiences can be.