How We Used Gemini to Automate Document Processing
Building an OCR service with Google Gemini that extracts structured data from documents, reducing manual processing time by 90% at SetupInSaudi.
At SetupInSaudi, we process hundreds of legal documents daily. Manual data extraction was becoming a bottleneck. Here's how we built an automated OCR service using Google Gemini that transformed our document processing workflow.
The Challenge
Our team was spending countless hours manually extracting data from various document types - contracts, Licenses, Certificates, invoices, legal forms, and more. The process was error-prone, time-consuming, and simply not scalable as our business grew.
The Solution
We decided to leverage Google's Gemini AI to build a comprehensive OCR service that could:
- Extract structured data from any document type
- Handle multiple languages (Arabic and English)
- Provide confidence scores for extracted data
- Integrate seamlessly with our existing workflows
Technical Implementation
We built the system using Node.js and the Google Gemini API. The architecture includes:
- Document preprocessing
- Intelligent prompt engineering for different document types
- Data validation and error handling
- Integration with our existing database systems
Results
The implementation was a game-changer:
- 90% reduction in manual processing time
- 95% accuracy in data extraction
- Support for 15+ document types
- Real-time processing capabilities
Lessons Learned
Building this system taught us valuable lessons about AI integration, prompt engineering, and building robust automation systems. The key was starting simple and iterating based on real-world usage.