Here's a clean, practical workflow to solve this effectively:
Workflow Solution:
You're looking at three core steps:
- 1.
Badge OCR extraction (Name, Title, Company)
- 2.
Linking Badge Data to Rep Name & Notes
- 3.
Pushing structured data into Clay
Step 1: OCR & Parsing
Your main issue is reliably extracting badge data. GPT-5 can do this, but it’s manual. Here's a better approach:
Reasoning: Automated, fast, scales well.
Manual GPT-5 upload for small volumes (10–30 badges). Fine in a pinch, but won't scale long-term.
Example OCR output would be:
{
"first_name": "Adam",
"last_name": "Rutkowski",
"title": "Sales Director",
"company_name": "Example Corp"
}
Step 2: Linking Data (Badge + Notes + Rep)
You want badge details PLUS rep assignment and voice notes in one place. Here's how:
Recommended tool: Airtable or Google Forms.
Google Forms is quicker; Airtable is richer.
Field 1: Badge image upload.
Field 2: Rep name (dropdown).
Field 3: Voice notes (recorded via mobile voice-to-text directly into a text field).
When someone fills out the form, Airtable automatically stores these three items together.
Step 3: Automating Integration into Clay
You now have structured badge info (via OCR) and corresponding notes and rep assignment (Airtable).
Zapier / Make Integration:
OCR API extracts badge data → writes structured data back to Airtable.
Airtable row updates trigger Zapier to auto-sync data into Clay.
Clay Table schema example:
| First Name | Last Name | Title | Company | Rep Name | Notes |
|------------|-----------|-----------------|--------------|----------|-------------------------------|
| Adam | Rutkowski | Sales Director | Example Corp | Nick | Interested, follow-up Monday |
This automatically keeps everything cleanly linked together.
Recommended Tech Stack (Long-term Solution):
OCR Engine: Google Vision API
Structured Form Capture: Airtable (badge upload, rep, voice-to-text notes)
Automation Platform: Zapier (connects Airtable + OCR API → Clay integration)
Why not just GPT-5? (Clarified)
GPT-5 is excellent but still manual (upload limits, human-in-loop).
For rapid trade-show workflows, automation and OCR APIs significantly outperform manual GPT-5 image handling.
Quick (Short-Term) Solution (MVP):
If you want speed right now with minimal setup:
- 1.
Upload images to GPT-5 (10 at a time), output structured text.
- 2.
Paste GPT output into Airtable or Google Sheet.
- 3.
Manually match notes/rep data from forms and push to Clay via CSV import.
My Recommendation:
For this customer scenario (trade shows, recurring use), go long-term OCR + Airtable + Zapier solution.
If immediate proof-of-concept needed today, GPT-5 works temporarily, but OCR-based automation wins hands-down long-term.
Let me know if you want detailed Zapier workflow templates or Airtable setup help.
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