Intelligent Document Processing (IDP)

• If you process different document layouts. An example could include: if the product number is not always in the same location, the use of machine learning is necessary.
• When it comes to differentiating one six-digit number from another six-digit number. Thanks to Acodis, content can be read and classified in seconds. 
• You want your own machine learning model. Acodis also offers you the opportunity to optimise a pre-trained ML model yourself and tailor it to your needs.
• In document processing, it's best to receive the highest quality results with reliable precision. Acodis always offers you the option of a manual quality check for peace of mind.

We can work with anything that can be transformed into a PDF. Therefore, we can handle paper, Scans, PDFs and any other electronic format.

Anything that is read from top left to bottom right. If you would like to process any other language, talk to us

• Can all present and future layouts be edited automatically?
• Is it a machine learning system or a simple rule-based system?
• Data protection: who has access to your machine learning model?
• How easy is the software for your future users to operate?
• Demo: were you able to test all of the required features or are they merely promises?

As of today, we can only extract limited information from handwritten documents. If you know of a technology that is able to do so on a broad scale, please let us know 😊.

We do not store any customer data for longer than it takes to process the document.

It is easiest to use Acodis in the cloud.

If you have special requirements for an on-premise solution, the service can be installed on your own machines via a docker container.

The minimum subscription period is 12 months, but you can always do upgrades (e.g. increase quota plan, add classification services etc.).

Document types typically have different data schemes (e.g. purchase orders, policy insurance or pension certificate). In contrast, document classes have the same data scheme but differ among classes in terms of the document's structure or layout (e.g. purchase order client A vs purchase order client B vs purchase order client C).

Data Points are individual information items, text fields or images, which get extracted from the document. For example, "Name", "Address, "Contract Number", "Delivery Date" etc.

Yes, the extracted data points can be validated automatically against customer-specific reference data or business rules.

Yes, you can benefit from automated data refinements (e.g. transform calendar dates into a standardized format).

The structured data can be enriched using external and internal data sources. E.g., Automated lookup of coordinates based on street addresses.