Cabinets need different workflows than doors.
Doors need different workflows than trim.
Jason was keeping track of this in his head. Then manually assigning tasks.
Phase 2 changed everything:
ChatGPT now classifies each product automatically.
Cabinets need different workflows than doors.
Doors need different workflows than trim.
Jason was keeping track of this in his head. Then manually assigning tasks.
Phase 2 changed everything:
ChatGPT now classifies each product automatically.
Short answer: Yes.
Jason's customers send orders through:
• Email text
• Screenshots
• Phone photos of invoices
• Forwarded messages
All different formats. All chaos.
Here's what we built in Phase 1:
An email triggers Make.com. ChatGPT reads EVERYTHING
Short answer: Yes.
Jason's customers send orders through:
• Email text
• Screenshots
• Phone photos of invoices
• Forwarded messages
All different formats. All chaos.
Here's what we built in Phase 1:
An email triggers Make.com. ChatGPT reads EVERYTHING
Jason runs a cabinet manufacturing business.
Every day, he's buried in emails, texts, and photos of orders.
Then he manually enters everything into spreadsheets. Assigns tasks. Tracks commissions.
2-3 hours. Every. Single. Day.
Jason runs a cabinet manufacturing business.
Every day, he's buried in emails, texts, and photos of orders.
Then he manually enters everything into spreadsheets. Assigns tasks. Tracks commissions.
2-3 hours. Every. Single. Day.
Not anymore.
Every new geography brings complexity. Different payment providers. Local regulations. New fee structures. More reconciliations.
The old model? Add analysts for each new market.
AI agents break that pattern completely.
Not anymore.
Every new geography brings complexity. Different payment providers. Local regulations. New fee structures. More reconciliations.
The old model? Add analysts for each new market.
AI agents break that pattern completely.
They assume your data is clean. Nice, structured tables. Standard formats. Perfect matching IDs.
That's not reality.
Real finance data is chaos. Scanned PDFs. Random email attachments. Payment exports with hundreds of thousands of rows.
They assume your data is clean. Nice, structured tables. Standard formats. Perfect matching IDs.
That's not reality.
Real finance data is chaos. Scanned PDFs. Random email attachments. Payment exports with hundreds of thousands of rows.
Google's Gemini 3 launch triggered an internal "code red" at OpenAI. The result? GPT-5.2 dropped December 11, 2025, faster than planned and more powerful than expected.
Google's Gemini 3 launch triggered an internal "code red" at OpenAI. The result? GPT-5.2 dropped December 11, 2025, faster than planned and more powerful than expected.
It happens more than you think.
Here's a real scenario: An invoice shows "refunded" in your system. But the payment provider has no refund record. Just a captured payment sitting there.
How does this slip through?
It happens more than you think.
Here's a real scenario: An invoice shows "refunded" in your system. But the payment provider has no refund record. Just a captured payment sitting there.
How does this slip through?