Every logistics and field-sales team runs the same expensive process: a driver photographs a receipt into a WhatsApp group, and a back-office clerk manually types the invoice number, total, and date into a spreadsheet. Hundreds of receipts a week = transcription errors and thousands of wasted hours.
AI vision models kill that bottleneck. Here's the pipeline that turns a blurry field photo into clean structured data in seconds.
OCR reads characters. Modern vision models (Claude Vision, Gemini Vision, GPT-4 Vision) read structure — they distinguish a tax ID from a total, and a date from an amount, even on crumpled, angled, or poorly lit receipts. No brittle per-vendor parsers.
WhatsApp image → Apps Script doPost → forward to vision model
→ model returns JSON { InvoiceNumber, TotalAmount, VendorName,
Date, Category, confidence_score }
→ confidence routing:
> 90 → auto-append to ledger
70–90 → flag for human review
< 70 → ask driver to re-photo
→ write row to Google Sheet (+ link to original image)
→ auto WhatsApp confirmation to driver
The confidence_score
is the whole trick — it's what stops bad extractions from silently polluting your ledger.
Pattern: Gemini for the first pass, escalate only low-confidence cases to Claude / GPT-4o.
~500 receipts/week: vision API $10–40 + WhatsApp API $30–60 + Apps Script free = ~$40–100/month. Versus a clerk at ~25 hrs/week = $2,000–4,000/month in loaded labor. Per-receipt cost: $0.005–0.02 (compress images to ~1024px to cut it further).
Accuracy: 92–97% on legible receipts, 75–85% on handwritten/damaged — hence the confidence routing.
The complete pipeline, categorization, and privacy controls are in the full guide on the MageSheet blog.
Built by the MageSheet team.