How a Hamburg manufacturer uses AI to auto-process orders

For a lot of mid-sized business owners, “AI-driven automation” sounds like something reserved for Silicon Valley giants with massive budgets. But these days? Powerful, intelligent automation is more accessible than ever.

I want to share the story of my client—a traditional manufacturing company based in Hamburg, Germany that produces high-quality mechanical parts—that worked with my agency, hagel IT-Services GmbH. When I connected with this client, their business was growing. But with that growth came a significant bottleneck: their order-to-invoice process was a slow, manual grind that cost them time and money, creating unnecessary stress.

By implementing a simple, AI-powered workflow, we cut the time they spent on order processing by over 70% and virtually eliminated costly data entry mistakes. Here’s how we did it.

The problem: Manual processing crushed productivity

Before we intervened, my client’s process looked a lot like what many businesses are still doing today:

1. A customer would send a purchase order as a PDF attachment to a general sales email inbox. And the formats varied wildly. Some were clean and computer-generated. Others, messy scans.

2. A member of the admin team would then open the email, download the PDF, and type all the relevant info into their enterprise resource planning (ERP) system…manually. This included the customer’s name, purchase order number, item codes, quantities, and shipping address.

3. The client would then have to double-check the item codes and pricing against a separate spreadsheet, since the ERP wasn’t always perfectly up to date.

4. After the order was entered and verified, they would manually generate an invoice in their accounting software, copying and pasting the data yet again.

5. Finally, the original email and PDF order would be manually filed away in a complex folder structure on their server.

Each order took, on average, 15–20 minutes to process. On a busy day with 30–40 orders, that’s one person’s entire workday spent on repetitive data entry. 

But the biggest problem wasn’t the time sink. One little typo in a part number or quantity could lead to the wrong product being shipped, causing costly returns and, worse, damaged customer relationships. 

The solution: AI-powered automation that handles varied PDFs

The managing director knew they couldn’t keep this workflow up. He didn’t want to hire another full-time employee just to enter data, either. He wanted his skilled team to focus on what they did best: serving customers and managing complex logistics, not copy-pasting text.

He’d heard about automation and Zapier, but he was skeptical. “Our orders are too inconsistent,” he told me. “The PDFs are all different. How can a machine read them?”

And that’s where the “AI” piece of the puzzle came in. We explained that modern AI models are incredibly good at reading and understanding unstructured documents, just like a human would. By building a Zap that uses AI, we could create a workflow that could handle the variation and automate the whole process from start to finish.

The five-step inbox-to-invoice workflow

We built a Zap that connects to Microsoft Outlook, Filter by Zapier, ChatGPT, Google Sheets, and QuickBooks.

Here’s the step-by-step breakdown.

A new order arrives

The process kicks off the moment an email with an attachment lands in this company’s dedicated orders@ inbox.

We set up a simple filter within the Zap to make sure it only runs for emails that contain specific keywords in the subject line—like “Purchase Order” or “New Order”—to avoid triggering on miscellaneous emails.

AI extracts structured data from messy PDFs

And then AI does its magic. Instead of a human opening the PDF, we send the email content and its attachment to ChatGPT.

We configured this step to send the raw text from the email body and, more importantly, the content of the attached file to the GPT model. The real trick is in crafting the right prompt. 

Our prompt looked like this:

“You are an expert order processing assistant. Read the following text extracted from a purchase order PDF and identify the following information: Customer Name, PO Number, Order Date, Product SKU, Quantity, and Shipping Address. Format your response as a clean JSON object. If any information is missing, use ‘N/A’ as the value.”

ChatGPT reads the document, understands the context, and pulls out the key data points, structuring them perfectly as machine-readable JSON. This instantly solves the problem of inconsistent PDF formats.

Order details flow into a spreadsheet

With the data neatly structured, we needed a place to log it and, if necessary, review it. Which Google Sheets is perfect for.

The Zap takes the JSON output from the ChatGPT step and maps each piece of data to a corresponding column in the spreadsheet: one column for Customer Name, one for PO Number, and so on. This created an automated, real-time log of every incoming order.

Invoices get generated instantly

The final step is to get this information into the company’s accounting system. Using the data from the newly created row in Google Sheets, the Zap automatically generates an invoice in their accounting software. It populates the customer name, adds the line items with the correct SKUs and quantities, and saves the entry for review.

We specifically configured the Zap to create a draft of an invoice. This keeps a human in the loop for a final, quick check before anything goes out the door. The process is automated, not unsupervised.

The results: 70% time savings and zero errors

My client’s new workflow now runs in the background, instantly processing orders as they arrive. Here’s a quick before-and-after comparison:

The admin team member who used to spend her entire day on data entry now spends about 30 minutes at the end of the day reviewing the draft invoices for accuracy before sending them out. This 90-second glance per order gives them complete quality control.

The 70%+ reduction in admin time freed her up to focus on high-value work: proactive customer communication, resolving shipping inquiries, and improving other internal processes. Cash flow improved because invoices went out the same day orders were received, not two days later. Most importantly, the stress and frustration of the manual process has vanished.

Time to tackle your own bottleneck

If your team is also bogged down by repetitive manual tasks, you don’t need a team of developers or a massive budget to solve your problems. Start by identifying just one bottleneck in your business. Is it processing orders? Onboarding new clients? Managing support tickets? 

Find the process that causes the most friction and follow this blueprint:

1. Identify the trigger: What event starts the process?

2. Map the steps: Write down every single action you take to complete the task.

3. Find your tools: See which of your existing apps connect to Zapier. (With more than 8,000 integrations, chances are, your favorites are already there.)

4. Build your Zap: Start simple. Automate one step at a time and build from there.

The power of AI orchestration is here, and it’s accessible to everyone. You just need to take that first step.