Over the past few months, I've been exploring how AI can move beyond chatbots and become an active part of business workflows.
That journey led me to build SmartStock AI, an inventory management platform that combines modern web technologies with AI agents, Retrieval-Augmented Generation (RAG), demand forecasting, and automation.
Instead of only tracking inventory, SmartStock AI helps businesses make proactive decisions.
What SmartStock AI Can Do
- 🤖 Forecast future product demand
- 📦 Recommend purchasing decisions through AI agents
- 📚 Answer inventory-related questions using Hybrid RAG with source citations
- 📄 Process invoices using multimodal AI
- ⚡ Generate real-time inventory alerts
- 🔐 Secure the platform with JWT authentication and role-based access control
Technology Stack
Frontend
- React 19
Backend
- Django 5
- Django REST Framework
Database
- PostgreSQL
- pgvector
AI
- LangChain
- Hybrid RAG
- AI Agents
- Prophet Forecasting
Infrastructure
- Celery
- Redis
- Docker
- GitHub Actions
What I Learned
Building SmartStock AI taught me much more than integrating an LLM into an application.
Some of the biggest lessons were:
- Designing AI features that solve real business problems.
- Building reliable agent workflows instead of simple chatbot interactions.
- Combining vector search with structured database queries.
- Managing asynchronous AI tasks using Celery and Redis.
- Creating production-ready APIs with Django REST Framework.
- Deploying and maintaining a modern full-stack application.
Demo
🎥 YouTube Demo
https://www.youtube.com/watch?v=DQJqs6bgE98
🌐 Live Demo
https://smart-stock-dev.vercel.app/
Demo Account
Email: viewer@smartstock.ai
Password: Viewer123!
💻 GitHub Repository
https://github.com/Eng-Ayman-Mohamed/SmartStock-AI
Final Thoughts
This project was developed as my graduation project during the Information Technology Institute (ITI) Full Stack Web & Generative AI Program.
It was an incredible opportunity to explore AI engineering, backend architecture, and modern software development while building a practical product.
I'm continuing to learn about Agentic AI, RAG systems, and intelligent automation, and I plan to share more technical articles about what I build and what I learn.
If you have any feedback or ideas for improving SmartStock AI, I'd love to hear them!
Top comments (2)
The useful part of this architecture is that the AI is tied to inventory decisions, not just chat. For something like purchasing recommendations, I would watch the handoff boundary closely: what evidence did the agent use, what assumption did it make, and what still needs a human approval before money moves?
Thanks! That's exactly why we kept a human-in-the-loop. The agent predicts stock-outs and creates a pending purchase order, but an administrator must review and approve it before the supplier is contacted. Explainability and clear decision boundaries are important parts of the workflow.