AI Rule Engine
- Rajeev Rai
Analytics
AI Business Rule
Gone are the days of laboriously sifting through data or grappling with intricate tools. Adeptia introduces an AI-driven feature, the AI Business Rule, which enables you to effortlessly extract valuable insights from a file using a straightforward command. It boasts an intuitive interface and robust algorithms that handle the heavy lifting, delivering actionable results within seconds, and ensuring precision and relevance in every output.
The AI Business Rule stands as an essential tool for businesses, providing the following key advantages:
Ease of Use: Business professionals can define rules directly in plain English, eliminating the need for technical knowledge or programming skills. This democratizes the creation and management of business rules, making it accessible to non-technical stakeholders.
Automating Decision-Making: These rules automate routine and complex decision-making processes based on data inputs and learned patterns.
Enhancing Accuracy: By using AI, these rules can process large volumes of data with high accuracy.
Quick Adaptation: The tool enables swift updates as business conditions or strategies change, without waiting for IT intervention. This flexibility allows organizations to remain agile and responsive to market dynamics.
Immediate Feedback: They offer automated testing and validation features that provide immediate feedback on the effectiveness and accuracy of new rules.
Creating a rule
Adeptia's AI Business Rule enables you to create rules—logical conditions that determine how data should be processed—with ease and precision by uploading a JSON file and consequently defining rules in plain English.
To create a rule:
On the Rules screen, click CREATE RULES.
On the Define Rule screen, click Browse Files to upload your JSON file on which the rule will be defined.
Click the Define Rule field.
Click ADD FIELD to select a field to which the rule will be applied.
You can view or replace the uploaded JSON file from the Define Rule screen.
In the Specify the data on which Rule will be applied section, click View or Replace File
to view the existing file or upload a new JSON file respectively.
For example, you have a JSON file containing the employee details, including fields such as ID, name, salary, etc. If you want to create a rule to verify whether the salary for any employee exceeds INR 80,000, you may select the salary field from the uploaded JSON record to define the rule as shown in the example screenshot.
Define the rule. For example, the rule to check whether the salary of any employee exceeds INR 80,000 may be defined as shown in the screenshot below. This rule will enable automated validation of employee salaries against the specified threshold. For examples of rule definitions, refer to this section.
In the Tailor Rule Result section, click ADD FIELD next to the Identifier fields to select the data fields that will serve as record identifiers in the result.
It is mandatory to select the Identifier 1 field for testing and generating rule results.
Click NEXT.
The Rule Details screen displays.
Enter a name and description for the rule.
Select Project.
10. Click Save.
11. On the Test Rule screen, click Use Data from Rule Definition to test the rule with the JSON file used to define the rule.
To test the rule with a new JSON file, select Browse Files.
The result is generated in the Rule Results section.
Once the result is generated you can perform any of the following actions:
OPTION | DESCRIPTION |
REPLACE FILE | Select this option to re-test the defined rule with a new JSON file. |
CLEAR | Select this option to clear the test results and re-test using either the existing file or a new JSON file. |
TEST AGAIN | Select this option to re-test the rule after making changes to the JSON file using the editor provided in the Test Rule screen. |
12. Click SATISFIED SAVE RULE.
This saves and activates a rule.
Business Rule examples
The following table contains examples of rule definitions.
In the Define Rule textbox, the added field represents its XPath. |
Sno | Data Validation Parameter | Rule description |
1 | The employee’s address is alphanumeric. | Check if the address of an employee is in alphanumeric format |
2 | The employee’s join date and birth date are in a valid date format. | Check if the join_date and birthdate of an employee is a valid date and consider all the possible date format. |
3 | The employee’s phone number is of the provided length. | Check if the phone_number of an employee contains 10 digit. |
4 | The employee’s marital status is one of the provided values. | Check if the marital_status of an employee is either "Single" or "Married". |
5 | The employee's years of experience must adhere to the specified conditions. | Check If an employee’s department is Marketing and their salary is greater than $75,000, then their experience_years must be provided; otherwise, it should not be available. |
6 | The employee's experience should be a whole number and must not exceed 15 years. | Check if the experience_years is not greater than 15 and should not contain a decimal value |