Backend
FastAPI
FastAPI is my default Python backend for AI products: async endpoints, typed request models, and generated docs.
How I use it
I use FastAPI as the service layer in front of models and LLM pipelines. It handles streaming responses, WebSockets and background jobs well, and the generated OpenAPI docs mean a frontend team can integrate without asking me.
Offered through
Shipped with FastAPI
All projects →
Dubbix
Cross-platform AI-powered audio and video processing app with Text-to-Speech, Speech-to-Speech, Accent Changer, Video Dubbing, and YouTube Summarizer

Shapes.inc
Platform to create AI-powered 'shapes' (models) with customizable traits like audio, personality, and reasoning, featuring Discord bot integration and private SDK
Questions clients ask about FastAPI
- Can FastAPI handle real-time features?
- Yes. Dubbix uses FastAPI with WebSockets for live audio and video processing status. Long-running model calls go to background workers so requests do not block.
- FastAPI or Node.js for my backend?
- If the backend calls models or does heavy data work, FastAPI, because the ML ecosystem is Python. If it is mostly CRUD and your team is JavaScript, Node.js. I build both.
Work with me
Need FastAPI work done?
I work remotely with clients across the US, Canada, the UK, the UAE, Brazil and Australia. Tell me what you are building and I will reply with questions, a plan and an estimate.
- Published in IEEE Access, 2025
- BSc CS, Gold Medalist
- Clients in 6 countries
- 10 shipped projects