Businesses deal with tons of paperwork on a daily basis that is unstructured and comes in a variety of templates. Some of these documents need to be reconciled with one another to ensure that the right items have been traded and payments are processed on time for them.
Organizations usually reconcile invoices with purchase orders and other documents manually. This can be highly time-consuming and additional resources may be required and hence, increasing the costs for the business as well.
KlearStack AI has developed rules engine feature that can help not only automate the reconciliation process for organizations but also transform and standardize data.
Table of Contents
What Exactly is Rule Engine?
The rules engine feature in KlearStack AI helps organizations set certain conditions and criteria. If the documents that are being processed fulfil these set conditions, only then the documents will be processed automatically, without any human intervention.
Rules engine features enable end-to-end document automation for organizations. This is highly beneficial for organizations as they have to spend less time processing documents and can focus on their core activities to grow their business. All the conditions can be easily set by the organization as per their requirements for their respective organizations.
Data Validation and Data Transformation are two types of rule engine features that KlearStack AI has to offer for a seamless and automated experience for processing documents
Figure 1: Data Validation & Data Transformation Conditions Set in KlearStack AI’s Rule Engine
Rules Engine Feature: Data Validation
The data validation type of rule engine feature enables organizations to process documents that match a certain set of conditions. These conditions are added by the organization themselves. These conditions allow documents to be either automatically processed or sent in an exception queue for manual check.
The data validation rule engine makes end-to-end document automation a reality and no intervention from personnel is required for it. The data validation rule engine feature can be applied for the header items in a table of a document as well as for line items or descriptions.
Data Validation Example
Figure: 2 is an image of an invoice that has been sent to an exception queue as it did not match two data validation rules that were set by the organization. This invoice will be checked manually as it does not fulfil a certain set of conditions that the organization has set in place.
The two data validation conditions set by the organization are:
- The Purchase Order number from the invoice should not be missing
- Invoices with amounts greater than 500,000 should be sent for a manual exception.
If these two conditions are not met, end-to-end document automation will not take place. If this particular invoice had a Purchase Order number and the total invoice value would be less than 500,000, then this invoice would have been processed automatically without any manual intervention.
This shows the importance of a data validation rule engine while processing documents and how it can make your business operations seamless and automated.
Rules Engine Feature: Data Transformation
The data transformation rule engine translates the data from various documents of suppliers and vendors to the language that is used in your inventory management system. This rule engine type of feature enables the standardization of data.
Just like in the case of the data validation rule engine, the data transformation rule engine can be applied for header items of the table in an invoice as well as line items or descriptions.
The data transformation rule engine plays a vital role in converting header items and line item text from multiple vendors and suppliers and enables standardization of it while the documents are being processed. Standardization helps in keeping the text uniform and makes it easy while look for items in an invoice on the system.
Another ability of the data transformation rule engine is that it can help in identifying if the items are outdated or not. This can be done by manually uploading a list of item codes or item descriptions that are now outdated and KlearStack AI’s data transformation rule engine will have the ability to cross-check whether the items are outdated or not.
Data Transformation Example
Example 1: Outdated Items
In Figure: 4, it can be seen that line item 1 and line item 3 are highlighted and line item 2 is not. Line item 1 and line item 3 are highlighted because, in the KlearStack AI, the data was manually added which stated that certain item codes are obsolete and no more in use. Cross-referencing from this list automatically, KlearStack AI was able to detect the item code from the invoice and highlighted these items in red, indicating that these item codes match the item codes from the list of obsolete items.
KlearStack AI’s data transformation rule engine is, therefore, beneficial to your organization to make you aware of what type of items are being traded and with whom. This can help your organization make better decisions going forward when it comes to choosing the right business partners for your organization.
Example 2: Transforming Data
As shown in Figure: 5, when the invoice was received, the line item descriptions of the item were extracted as it is from the invoice. With the data transformation rule engine in place, you can customize, standardize and translate the data to the one that is used by your inventory management system, as shown on the right side of the Figure: 5 image.
ALLEGRA 180 MG 1X10’S was transformed to ALLEGRA 180 MG TAB, ALLEGREA NASAL SPRAY 120 MDI was translated to ALLEGRA NASAL SPRAY and so on.
This transformation of data is beneficial for organizations as it not only standardizes item descriptions but also makes it easy for cross-referencing in the future. The item descriptions can be transformed in the same original field or a new column can be created in which the transformed data appears.
Advantages of Rules Engine Feature for Your Organization
Easy Cross Verification of Documents:
The data validation rule engine feature allows your organization to automate the process of checking one document with another and avoid manually cross-checking documents. This makes cross-checking of documents seamless and error-free.
Standardization of Unstructured Data
The data transformation rule engine standardizes the language used while processing the documents, irrespective of from which supplier or vendor partner the document has come. This allows your documents to be standardized automatically without manually changing line-item descriptions.
No Human Intervention
Since data validation and data transformation are done automatically, there is no need for any manual intervention to cross-check the documents or translate the descriptions to the ones that are used by your organization. All this can happen automatically and accurately.
End-to-end automated document automation means that your organization will have the ability to focus on the core functions of your business and need not worry about processing documents. As more documents are to be processed during peak business seasons, this will not hamper your productivity as the platform can process tons of documents without any hassle.
Learn more about How Does Rule Engine Feature Work in KlearStack AI
KlearStack Continues to be a Leader in IDP
With the release of data validation and data transformation rules engine features, KlearStack’s platform has upgraded itself to the next level of document processing automation. Classifying, extracting and digitizing documents using advanced OCR made Intelligent Document Processing (IDP) achievable. With rules engine features, an additional layer of automation has been added wherein apart from extraction and transfer of data on the cloud, documents can be processed end-to-end without any manual inspections.
The rules engine feature of KlearStack AI allows organizations to become much more agile and scalable which means they can focus more on growing their business and less on operations of managing invoices, receipts and payments. End-to-End automated documentation also provides client satisfaction to vendor partners as they are assured that their payments will be cleared on time as invoices, purchase orders and other documents are processed on time. Therefore, along with IDP, the additional layer of automation in KlearStack’s platform can help your organization retain existing partners.
CEO’s Take on Need for Rule Engine Feature in KlearStack AI
Ashutosh Saitwal, Founder & CEO, KlearStack states that the rules engine feature was always in pipeline and through their various discussions with clients, they realised the need to go beyond data extraction.
“It was always on our roadmap. From discussions with our partners, we realised that there is a need for organizations to have configurable business rules. Every organization has different SOPs and therefore, we understood that each business needs to have the ability to put their own rules in place to process documents. We also realised that there was a demand for solutions that goes beyond data extraction from documents. Extraction should not be the end. Document intelligence should go beyond data extraction.”