Affinda has developed our data extraction solution to work either within our dedicated app or embedded within your platform. With the embedded option, workflows can remain largely unchanged to current processes with customisations available so that the tool fits seamlessly into your platform.
The validation interface can be customised to seamlessly fit within your platform. For more information on white-labeling and other customisations, see here.
How to embed the validation interface?
- Invoices posted to Affinda will extract and return via API most of the data from an invoice
- Within this API response, we return a value called meta.reviewUrl
- This is a signed and authenticated URL which can be embedded as an iFrame within your platform and allow members of your team to validate the data predicted and add any additional data points
- Once an invoice is validated, the validated data can be requested and entered into your platform with full confidence in the accuracy
Each signed URL is valid for 60 minutes and as such we recommend not storing the URL locally. If customers want to access the validation tool for an invoice that has already been created in the Affinda system, we recommend retrieving the new URL only when the user clicks to validate the invoice by performing a request to /invoices/<identifier> which then sends the user to a page that embeds the URL retrieved from meta.reviewUrl.
Why should I use the validation interface?
There are two key advantages of using the validation interface.
- Ensuring the integrity of data
Despite all of the benefits of using an AI-based engine for extracting data, the reality is that no model will ever be 100% accurate for every single invoice. The benefits of a 'human in the loop' model mean that predictions made by the model are validated and 100% accurate data can be guaranteed before entering into your system for further processing.
The ease of use of the interface, and the already high accuracy of the model predictions, means that obtaining perfect data is now a matter of seconds, not minutes, per document.
- Creation of a feedback loop
One key strength of AI models is that they can continue to learn over time. When using the validation interface, the data from these human corrections can be fed back into our models so that our engine can begin to learn different suppliers' invoice formats and accuracy will continue to improve over time, further reducing the amount of human intervention required.