Auto Data AI

Inspiration

Every day, millions of businesses waste hours copying and analyzing information from invoices, contracts, and reports. I wanted to build a solution that could automatically extract and organize data from any PDF using AI — fast, reliable, and accessible from the cloud.

What it does

Auto Data AI reads PDFs, detects key information such as vendor names, amounts, and dates, and returns clean structured data instantly through an API.
It helps e-commerce platforms, accountants, and analysts automate document handling and reduce manual work.

How I built it

The backend is powered by FastAPI and deployed on Render Cloud for high availability.
I used PyMuPDF (fitz) to extract text and layout information, then added lightweight parsing logic to detect key fields.
The API is documented with Swagger UI, so anyone can upload a PDF and get real-time JSON results.

Challenges

Handling PDFs with mixed layouts, fonts, and tables was difficult. I improved accuracy by cleaning the extracted text and adjusting field-detection rules.
Deploying and managing dependencies on Render also required troubleshooting (especially the PyMuPDF module).

Accomplishments

✅ Live API running at https://auto-data-ai.onrender.com
✅ Fully documented endpoints with Swagger UI
✅ Tested successfully on invoices and business documents

What’s next

• Integrate decentralized file storage (IPFS or Substrate) using Polkadot Cloud
  
• Add user authentication and billing for commercial API use
  
• Support OCR for scanned PDFs and multilingual extraction

Built With

• cloud
• fastapi
• integration
• planned
• polkadot
• pymupdf
• python
• render-cloud
• swagger-ui