Wanted to share a practical AI implementation we did recently.
**The Challenge:**
Clients were sending invoice photos via Telegram. Image quality was all over the place:
- Bad lighting and skewed angles
- Creased or folded documents
- Washed-out or blurry text
- Standard OCR would fail constantly
**The AI Solution:**
Built an automated pipeline:
**Input:** Telegram bot receives invoice photos
**Processing:** Gemini Vision API extracts structured data (invoice number, date, amount, vendor, line items, etc.)
**Validation:** Auto-format and validate extracted fields
**Output:** Push clean data to Google Sheets
All orchestrated through n8n workflow automation.
**Key Learnings:**
- Vision models handle poor image quality far better than traditional OCR
- Gemini Vision was surprisingly accurate even with heavily distorted images
- Structured prompting is critical for consistent field extraction
- Adding validation rules catches edge cases that AI misses
**Results:**
- Near-instant extraction vs hours of manual work
- Accuracy remained high despite image quality issues
- Scaled operations without adding headcount
Anyone else working on vision-based document extraction? Curious what models/approaches you're using.
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