This video demonstrates a custom AI assistant I built and trained on my own professional background. Instead of a static CV, this 'digital twin' provides a dynamic and interactive way to explore my skills, project history, and hands-on experience.

The goal was to create a personalized, context-aware knowledge base—think of it as ChatGPT, but trained exclusively on my career data.

💡 What to Look for in the Demo

The video showcases several key capabilities:

• Persona & Knowledge: The AI correctly identifies its purpose and answers specific questions about my projects by referencing its custom training data.
  
• Real-World Application: It performs a practical task by analyzing a real-world job description and evaluating my suitability, providing an evidence-based rationale.
  
• Architectural Flexibility: I demonstrate the system's modular design by instantly swapping the core LLM engine from OpenAI's GPT to a high-speed model from Groq, showing how the architecture can adapt to different performance and cost requirements.

🚀 Why This Technology Matters

This project isn't just a personal demo; it's a proof-of-concept for powerful enterprise applications. Custom-trained LLMs like this one represent a major shift in how businesses can leverage their internal knowledge:

• 🧠 Trained on Your Data: LLMs can be tailored to ingest private, domain-specific content like project files, internal policies, or historical data.
  
• 📊 Always-On Expertise: They offer instant, contextual answers without waiting for a human subject matter expert.
  
• 🔒 Private & Secure: When deployed within a corporate environment, they can sit behind firewalls and respect strict data boundaries.
  
• 🤝 Enhanced Experience: They can be trained on documentation and brand voice to become trusted interfaces for customer support, HR, or new employee onboarding.