• There are no limitations for the pdfs as long as the pdfs are in the same format in one model.
• Size of the image should be less than 4mb

Yes

After the training is done, we can see the average accuracy of the model including the individual %s of the fields.

Yes, this falls under the managed service type where we extract the data and perform end- end operation for the customer.

Yes

No... As many templates are present, those many models/projects need to be there.

Based on the client's requirement, we can provide the error log link to the customer. Like an e-mail notification, or a text message with the detailed info of error files.

No, one can use this tool on a tenant basis. All the extracted data is available on our cloud.

Yes

Yes

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A simple JSON message to data from your existing RPA system can be fed as input to our AURA DCap and the output can be written to customers’ existing database and the third-party application for data storage.

Processing time takes up to 5 minutes.

500 is the limit.

There is no limitation, more people may use it in parallel.

We recommend that you use five manually labelled forms of the same type to get started when training a new model and add more labelled data as needed to improve the model accuracy.

No

File Size: < 4 MB.

Number of Pages: < 50

Minimum Image Size: 50 x 50 Maximum Image Size: 4200 x 4200

No

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English, Chinese (Simplified), Dutch, French, German, Italian, Portuguese, Spanish.

JSON, XML

Any web browser.

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Printed and handwritten forms, PDFs, and images.

Training % depends on the format of the pdfs / positions of the fields you're interested in. The same kind of pdfs should be uploaded in a particular model/project for the % to increase

Due to some reasons, like a different format than the trained documents, or quality enhancement issues, some docs could not get processed. Those docs sit in our error queue. The support user gets notified when this happens, and the user can retrain documents and successfully process them.

This we can say based on the complexity of the documents, some are easy to understand and some need extra processing if they have minute details that are hard to capture. We can say a minimum of 4 weeks to maximum 6 weeks.

5 PDF

• Checking the extracted data to master data.
• Identifying the checkboxes are a difficult task coming to the docs, aiAURA DCap uses AURA and Machine learning techniques which enhance the quality of the document allowing even the minor details to be captured with ease.

Based on the customer requirement, we can save the documents in our database until the docs are processed. Some customers drop the docs in their SharePoint and our team can upload the docs directly from the SharePoint. So based on the customer's use case, we can save the docs in our database.

East us 2

All the trained models get stored in azure blob storage. As long as the models are present in the blob storage, we can recover them.

A general resource with a non-technical background can understand the aiAURA DCap UI and can process the documents. It saves a lot of money and 90% of manual efforts are replaced by automation.