Use the invoice processing prebuilt model in Power Automate

1. Sign in to Power Automate.

2. Select My flows in the left pane, and then select New flow > Instant cloud flow.

3. Name your flow, select Manually trigger a flow under Choose how to trigger this flow, and then select Create.

4. Expand Manually trigger a flow, and then select +Add an input > File as the input type.

5. Replace File Content with My invoice (also known as the title).

6. Select +New step > AI Builder, and then select Extract information from invoices in the list of actions.

7. Specify My invoice from the trigger in the Invoice file input.

   

8. In the successive actions, you can use any of the invoice values from the model output.

   

Congratulations! You've created a flow that uses the AI Builder invoice processing model. Select Save on the top right, and then select Test to try out your flow.

Page range

For large documents, it's possible to specify the page range to process.

You can enter a page value or page range in the Pages parameter. Example: 1 or 3-5.

Note

If you have a large document with only one invoice, we strongly recommend to use the Pages parameter to aim at your invoice, and therefore reduce the cost of model prediction and increase performance. However, the page range should contain a unique invoice for the action to return correct data.

Example: A document contains a first invoice in page 2 and a second invoice that spans over pages 3 and 4:

• If you enter page range 2, it will return the data of the first invoice.
• If you enter page range 3-4, it will only return the data of the second invoice.
• If you enter page range 2-4, it will return partial data of the first and second invoices (should be avoided).

Parameters

Input

Name	Required	Type	Description
Receipt file	Yes	file	The invoice file to process
Pages	No	string	Page range to process

Output

Name	Type	Definition
Amount due (text)	string	Amount due as it's written on the invoice
Amount due (number)	float	Amount due in standardized number format. Example: 1234.98
Confidence of amount due	float	How confident the model is in its prediction. Score between 0 (low confidence) and 1 (high confidence).
Billing address	string	Billing address
Confidence of billing address	float	How confident the model is in its prediction. Score between 0 (low confidence) and 1 (high confidence).
Billing address recipient	string	Billing address recipient
Confidence of billing address recipient	float	How confident the model is in its prediction. Score between 0 (low confidence) and 1 (high confidence).
Customer address	string	Customer address
Confidence of customer address	float	How confident the model is in its prediction. Score between 0 (low confidence) and 1 (high confidence).
Customer address recipient	string	Customer address recipient
Confidence of customer address recipient	float	How confident the model is in its prediction. Score between 0 (low confidence) and 1 (high confidence).
Customer ID	string	Customer ID
Confidence of customer ID	float	How confident the model is in its prediction. Score between 0 (low confidence) and 1 (high confidence).
Customer name	string	Customer name
Confidence of customer name	float	How confident the model is in its prediction. Score between 0 (low confidence) and 1 (high confidence).
Due date (text)	string	Due date as it's written on the invoice
Due date (date)		Due date in standardized date format. Example: 2019-05-31T00:00:00Z
Confidence of due date	float	How confident the model is in its prediction. Score between 0 (low confidence) and 1 (high confidence).
Invoice date (text)	string	Invoice date as it's written on the invoice
Invoice date (date)	date	Invoice date in standardized date format. Example: 2019-05-31T00:00:00Z
Confidence of invoice date	float	How confident the model is in its prediction. Score between 0 (low confidence) and 1 (high confidence).
Invoice ID	string	Invoice ID
Confidence of invoice ID	float	How confident the model is in its prediction. Score between 0 (low confidence) and 1 (high confidence).
Invoice total (text)	string	Invoice total as it's written on the invoice
Invoice total (number)	float	Invoice total in standardized date format. Example: 2019-05-31T00:00:00Z
Confidence of invoice total	float	How confident the model is in its prediction. Score between 0 (low confidence) and 1 (high confidence).
Purchase order	string	Purchase order
Confidence of purchase order	float	How confident the model is in its prediction. Score between 0 (low confidence) and 1 (high confidence).
Remittance address	string	Remittance address
Confidence of remittance address	float	How confident the model is in its prediction. Score between 0 (low confidence) and 1 (high confidence).
Remittance address recipient	string	Remittance address recipient
Confidence of remittance address recipient	float	How confident the model is in its prediction. Score between 0 (low confidence) and 1 (high confidence).
Service address	string	Service address
Confidence of service address	float	How confident the model is in its prediction. Score between 0 (low confidence) and 1 (high confidence).
Service address recipient	string	Service address recipient
Confidence of service address recipient	float	How confident the model is in its prediction. Score between 0 (low confidence) and 1 (high confidence).
Shipping address	string	Shipping address
Confidence of shipping address	float	How confident the model is in its prediction. Score between 0 (low confidence) and 1 (high confidence).
Shipping address recipient	string	Shipping address recipient
Confidence of shipping address recipient	float	How confident the model is in its prediction. Score between 0 (low confidence) and 1 (high confidence).
Subtotal (text)	string	Subtotal as it's written on the invoice
Subtotal (number)	float	Subtotal in standardized number format. Example: 1234.98
Confidence of subtotal	float	How confident the model is in its prediction. Score between 0 (low confidence) and 1 (high confidence).
Total tax (text)	string	Total tax as it's written on the invoice
Total tax (number)	float	Total tax in standardized number format. Example: 1234.98
Confidence of total tax	float	How confident the model is in its prediction. Score between 0 (low confidence) and 1 (high confidence).
Vendor address	string	Vendor address
Confidence of vendor address	float	How confident the model is in its prediction. Score between 0 (low confidence) and 1 (high confidence).
Vendor address recipient	string	Vendor address recipient
Confidence of vendor address recipient	float	How confident the model is in its prediction. Score between 0 (low confidence) and 1 (high confidence).
Vendor name	string	Vendor name
Confidence of vendor name	float	How confident the model is in its prediction. Score between 0 (low confidence) and 1 (high confidence).
Detected text	string	Line of recognized text from running OCR on an invoice. Returned as a part of a list of text.
Page number of detected text	integer	Which page the line of recognized text is found on. Returned as a part of a list of text.

Related topics

Invoice processing overview