🚀 AI 앱뿐만 아니라 실제 AI 엔지니어링 시스템을 구축하고 있습니다.

Most AI projects I see today are simple wrappers around APIs.

You call an LLM → get a response → call it “AI app”.

But I wanted to go deeper.

I’m currently building real AI engineering systems — where AI is just one part of a full backend architecture, not the entire product.

🧠 What I’m building

I’m working on multiple AI projects like:

• 🤖 AI personal assistant (Friday Assistant)
• 🧠 Multi-agent productivity system (NOVA)
• 🇩🇪 AI German learning PWA (Sofort German)
• 📚 RAG-based study assistant (StudyRAG)
• 🍽️ AI food intelligence app (FoodSight AI)

But the goal is NOT just features.

The goal is:

Building production-style AI systems with real engineering concepts.

⚙️ What makes these different

Instead of just “using AI”, I’m focusing on:

🏗️ System architecture

• Backend services (FastAPI)
• Modular AI pipelines
• Separation of AI logic and application logic

🧠 AI engineering layer

• Agent-based workflows
• RAG pipelines (retrieval + generation)
• Tool calling systems
• Memory systems (short-term + long-term)

💾 Data + state handling

• Databases for persistence
• Vector databases for semantic memory
• Structured data flow between components

⚡ Real-world constraints

• Latency handling
• Async processing
• Failure handling (what if AI fails?)
• Cost-aware design decisions

🔥 Why I’m doing this

I don’t want to build “AI demos”.

I want to build systems that behave like real products.

Systems that:

• Scale
• Fail gracefully
• Have architecture
• Can be explained clearly in interviews
• Solve real-world problems

🧪 My current focus

Right now I’m in the process of:

• Turning prototypes into proper backend systems
• Improving architecture design
• Adding real engineering structure to AI workflows
• Making everything explainable and production-ready

📌 What I’ll share next

I’ll start documenting:

• Architecture breakdowns 🧠
• System design decisions ⚙️
• AI engineering concepts used in real projects 🔥
• Failures and debugging stories 🐞
• Live demos of working systems 🚀

💬 Why I’m posting this

I want to:

• Share my journey openly
• Connect with other AI engineers
• Learn from real-world feedback
• And build in public while improving every system I create

🚀 Final thought

AI is not just about prompts.

Real value comes from:

engineering systems that use AI as a component, not the entire product.

This is what I’m building toward.

If you’re also working on AI systems, I’d love to connect and learn from your work.