Financial Documents

ElinarAI helps automating processes

Elinar has specialized text analytics modules available for several industries and use cases. In the World full of financial documents ElinarAI can be useful when automating processes for example with orders, invoices or cash allocation. Especially in large companies where thousands of orders/invoices may still be handled manually, we can help you achieve great savings when automating processes with AI. Read more…

Talk more with our expert:
Elinar's CTO Ari Juntunen, specialist in augmenting and automating with Artificial Intelligence (AI)
Ari Juntunen
Chief Technology Officer
+358 40 524 4482

ElinarAI can help you achieve great savings.

AI Powers up sales order processing

Leveraging AI capabilities can dramatically enhance effort-to-value-ratio of sales order processing

Sales orders come in countless forms. Orders come as email and someone has to process them into ERP system. It takes routine human effort to scan these orders and pick up the essential information for further processing.

You can do sales order processing in several different ways. But the most expensive relies on human labor to extract data from sales orders and enter this data to ERP.

We can also automate this process

Though traditional capture solutions can also be quite inefficient. This is due to a large number of unique customers sending in sales orders.

For a large international corporation it is not unheard to have much over 20 000 corporate customers that have multiple locations each. In a worst-case scenario, there will be 400 000 unique sales order templates, that someone has to implement by capture solution. If each template takes a dozen hours, total effort will be over half a million hours. And that isn’t obviously a good business case.

You can give these routines for handling to artificial intelligence. And, at the same time, save the human effort for more meaningful and creative tasks.

Enter Elinar.AI

With our help, you can achieve efficient sales order processing by combining IBM Datacap Insight Edition with Elinar.AI. The solution uses powerful cognitive capabilities of Datacap to create a page layout and tokenize Sales Orders.

Elinar.AI combines IBM Watson with Elinar proprietary AI for an advanced understanding of content-centric processes. This will take Enterprise Content Management (ECM) onto the next level by leveraging advanced artificial intelligence and machine learning methodologies.

Training artificial intelligence

Initially, we train AI to understand various sales orders. For this, we need order and invoice history and give to AI a large number of documents. It takes several weeks of super computer capacity for AI to learn sales order differences. We use Power8 Minsky for this.

After the training AI for different sales orders, it learns to recognize them. If a company gets new partners – new sales orders form that is – a few samples would initially be run through AI to see if it performs correct. Otherwise we need some further training or data post-processing.

AI enhanced sales order processing

When a new order comes in it is scanned with Datacap optical character recognition. After that the trained AI model can point out the correct information from the order.

Datacap is further used for validation and verification of the information. After that it extracts the correct information and enters it to ERP or some other system.

Benefits and challenges summed:

  • Traditional sales order processing takes up a huge amount of time with routine work.
  • Large international companies might have 20 000 different customers with various different sales order forms.
  • Using traditional capture solutions it might take even hundreds of thousands of hours work to implement all the sales order templates into the system.
  • When combining capture with Artificial Intelligence a lot of work effort will be saved for more meaningful tasks.

Many routines can be given for artificial intelligence to be handled – the human effort is saved for more meaningful and creative tasks.