Email is the most common method for receiving third-party invoices, but it comes with it’s share of drawbacks, such as manual data entry from PDFs, the need to download attachments, and high layout variability that complicates automated parsing and data extraction. In this article, we will explore approaches to overcoming these challenges using Zapier and no-code invoice parsing tools. Additionally, we will provide an example of how to leverage the newest AI-powered OCR solutions to extract invoice data from emails and seamlessly send it to Google Sheets.
Software Engineer
Automating invoice extraction from emails in Zapier requires the right parsing tool. The best approach depends on factors like invoice format, volume, and the need for accuracy. Below, we compare three main solutions: AI-powered OCR tools, PDF.co’s invoice OCR, and Zapier’s built-in Email Parser.
These tools streamline data extraction for mid to high-volume invoice inflows with varied layouts. They feature AI-powered OCR, error handling, data formatting, and pre-built integrations for seamless automation. Examples with Zapier connectors include Cradl AI, Docparser and Nanonets.
These tools offer pre-trained AI models for instant invoice parsing, with retraining options to improve accuracy over time. Built-in validation and error handling ensure safe, scalable invoice parsing automation with minimal human intervention. They also support various document types beyond invoices.
For invoices with predictable structures and lower volumes, these tools may be more advanced than necessary. In such cases, simpler, rule-based tools might be a more cost-effective alternative.
The best option for businesses processing mid-to-high high volumes of invoices from multiple vendors with varying formats. Their built-in validation features, such as human-in-the-loop review, make them a reliable choice for automating business-critical document workflows while ensuring data accuracy.
PDF.co provides a range of PDF-related automation tools, including OCR for extracting invoice data from PDFs. Unlike most of PDF.co’s functionality, which relies on predefined templates, its AI-powered invoice OCR model allows data extraction from invoices with variable layouts.
While PDF.co is largely template-based, its AI-powered invoice OCR is an exception, making it more flexible for invoices that do not follow a fixed format. This allows businesses to extract invoice data without needing to manually define templates for each invoice.
PDF.co’s AI model lacks a mechanism for users to refine accuracy, making it difficult to address inconsistent results. Without built-in validation, it becomes unreliable for processing large volumes of financial documents, as errors may go unchecked and lead to critical inaccuracies in essential data.
Best suited for businesses handling smaller invoice volumes where manual verification of extracted data is feasible. If accuracy is a primary concern, extracted data should be reviewed before being used in financial workflows.
Zapier offers a built-in Email Parser designed to extract structured data from email bodies and use it within automation workflows.
Email Parser by Zapier is free and easy to set up. Users can define simple parsing rules to extract key details from structured email content, making it a straightforward solution for specific use cases.
This tool does not support OCR, meaning it cannot extract text from PDF or image attachments. Additionally, it relies on structured email formats, requiring emails to follow a predictable pattern. If invoice details are presented inconsistently or sent as attachments, this method will not work.
This is a viable option for extracting structured plain-text invoices directly from email bodies. However, in practice, invoices are rarely sent as plain text, making this tool impractical for most real-world invoice processing needs.
Let’s see an actual Zapier automaton example of how to extract invoice data from emails to Google Sheets.
Before we begin, make sure you’ve created a Cradl AI account.
Once you’re logged into Cradl AI, create your first AI model with just a few clicks by cloning the invoice model.
Customise the model to suit your needs by adding or removing fields based on the data you want to extract from your invoices. Save your model, and that's all you need to do to create an AI model that understands your invoices.
Got tabular data or line items in your invoices that you also want to extract? No problem - just use the Line Items field. For more information on that, this article goes into detail about data extraction from tables.
There are several ways to import your invoices into Cradl AI for data extraction.
Connecting your mailbox to Cradl AI with a forwarding address is an easy and efficient way to automate your imports. Cradl AI auto-generates an email address you can forward your invoice emails to - any email attachment that arrives at this address will be automatically parsed.
Manually downloading your invoices and uploading them to Cradl AI in bulk is always an option for those who prefer a simple and manual approach. This works particularly well if you only process invoices occasionally.
Zapier can monitor your mailbox (i.e., with Gmail's New Email trigger) for trigger events—such as emails moved to a specific folder or titles containing certain keywords—and send them to the AI tool for processing. If you choose this approach, you want to add Cradl AI's Parse Document or Parse Document With Human-In-The-Loop actions.
In this example, we'll go with the Email forwarding approach.
Test the integration by sending an email with a document attached to the newly generated address. The document will be automatically processed, and you can view the extracted data inside the Validator in Cradl AI.
Now that your AI model is working, we'll connect it with a Google Sheet.
Head over to your Google Drive and create a blank spreadsheet. Add headers that correspond with the fields you want to extract from your documents. In Google Sheets you add headers by simply typing values into the topmost cells:
When you're mapping Cradl AI's extracted data fields to your spreadsheet's headers, you'll notice that you can choose from way more fields than the handful you defined in your spreadsheet's headers.
99% of the time you are looking for those values that are prefixed with Validated Predictions and suffixed with Value, such as Validate Predictions Purchase Date Value, Validate Predictions Total Amount Value, and so on.
Once your Zap is activated, the automation is ready to run:
Clicking Validate triggers the Zap, sending the data directly to your Google Sheet. Within seconds, your spreadsheet will update with the extracted values.
If you would like a version of this tutorial that does into more detail, this video has got you covered.
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