Adeptia Business Rules Engine FAQ's

What is the purpose of the AI Rules Engine?

The AI Rules Engine is designed to automate the code generation process for workflows driven by specific business rules or logic. It streamlines large-scale data transformations by leveraging AI to interpret, map, and generate the necessary code based on predefined rules and logic. This helps improve efficiency and accuracy in rule-based workflows by automating the creation of complex logic.

What use cases and scenarios are better suited for AI Rules Engine, and what use cases and scenarios are better suited for Traditional Mapping Rules?

The AI Rules Engine is better suited for handling complex scenarios where:

1. Business users can easily create and validate/test complex rules with multiple datasets for accuracy.

2. The system allows users to select nested or hierarchical data patterns without requiring technical expertise. Additionally, users can effortlessly add multiple identifiers when creating rules.

3. Data patterns are not easily identifiable, and AI-based logic can dynamically recognize and adapt to complex relationships within datasets.

4. Large datasets require dynamic rule generation, where manual intervention is inefficient.

5. Automated decision-making processes need AI-based logic, enabling real-time analysis and adjustments based on evolving data trends.

6. Scalability and flexibility are crucial in rule generation, ensuring the system adapts as business and data complexity grow.

In contrast, Traditional Mapping Rules are better suited for:

1. Simple, predefined, and repetitive mappings that don’t require advanced dynamic logic.

Straightforward rule-based transformations where data changes are predictable and don’t demand flexibility or adaptation.

What is the difference between AI Rules Engine Auto-Mapper (Local) and Auto-mapper (AI Based)?

• Auto-Mapper (Local) uses predefined rules for mapping fields between datasets, relying on static logic and manual configuration.

• Auto-Mapper (AI-Based) leverages AI and machine learning to automatically predict and create mappings based on data patterns, reducing manual intervention.

Will the AI Rules Engine run on both Windows Adeptia environment and Linux Adeptia Environment?

Yes, the AI Rules Engine is designed to run on both Windows and Linux environments within Adeptia.

 What version of AC supports AI Rules Engine?

This functionality is typically available in newer versions with AI capabilities, specifically in AC 4.3 and above.

Can AI Rules Engine be added to Previous AC version Deployments?

AI Rules Engine is more suited for Adeptia Connect 4.3 and above, where AI capabilities are fully integrated. It does not support the older versions of AC.

 Which Adeptia Team is responsible for maintaining the AC Rules Engine?

The R&D team will be responsible for all new enhancements, while the MST team will handle any support-related responsibilities.

 Can AI Rules Engine be used externally from any other application as a Service or is it embedded within Adeptia and is only used from within the Adeptia solution?

The AI Rules Engine can be used externally as a service, but it is primarily designed to be embedded and utilized within the Adeptia solution itself.

What are the limitations of AI Rules Engine?

Some key limitations mentioned in the document include:

• The specific formats it supports (e.g., JSON for input/output).

• Potential dependency on Azure OpenAI Services.

• Not every workflow or scenario may be suitable for AI-based automation, particularly if the task is simpler and can be handled by traditional methods.

 What are the Infra., Configuration, and Access Prerequisites for communicating with Azure Open AI? Are there specific ports to be opened up or IP Whitelisting to be done or Certificate to be configured?

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Th following infrastructure, configuration, and access prerequisites for communicating with Azure Open AI. These include:

• Ports: Specific ports need to be opened to allow communication with Azure services.

• IP Whitelisting: You may need to whitelist certain IP addresses to enable secure communication with Azure.

Certificates: In some cases, certificates may need to be configured to establish secure connections to Azure Open AI services.

Is AI Rules Engine Deployed as part of the AC Installer or is it installed separately after AC is installed?

Currently, The AI Rule Engine will be deployed separately.

What Azure subscription is used for OpenAI Rules Engine?

The Azure subscription used for the OpenAI Rules Engine depends on the customer’s specific setup. In some cases, Adeptia may provide an Azure subscription for cloud environments, but the document does not specify further details on the subscription type.

Does the customer need to provide their own Azure Account to use AI Rules Engine for their AC Cloud Environment?

• Yes, customers may need to provide their own Azure account when using the AI Rules Engine in an AC Cloud environment. This would allow the customer to have more control over their Azure resources and costs.

Does the customer need to provide their own Azure Account to use AI Rules Engine for their OnPrem AC Environment?

• For an OnPrem AC Environment, the customer is expected to provide their own Azure account to access the AI Rules Engine, as the OnPrem setup does not include automatic access to cloud-based resources unless configured by the customer.

 What is the role of AI in AI Rule Engine?

The AI in the AI Rules Engine plays a pivotal role in automating the generation of rules and code. It leverages machine learning models to interpret complex data patterns and create rules for data processing and decision-making.

What is the AI technology used behind the scene for generating the code?

The AI technology used includes Azure OpenAI services, which leverage Generative AI models like GPT to assist in the generation of rules and logic during workflows.

What Gen AI LLM is used in Azure Open AI for AI Rules Engine?

The AI Rules Engine uses Generative AI Language Models (LLM) such as GPT models provided by Azure OpenAI Services. These models assist in code generation and rule automation based on data inputs.

How does the AI Rules Engine fit in within the Process Flow, Template, or Transaction (Automation)? Is the AI Rules Engine represented as an activity?

The AI Rules Engine is represented as an activity within process flows or transactions. It integrates into the workflow template by dynamically generating rules and automating tasks based on data inputs and predefined configurations.

What software language is the AI Rules Engine code generated as?

The AI Rules Engine typically generates code in Python, which is the primary language used for rule execution.

 Is Python the only code option, or are there other languages supported for the AI Code Generator?

While Python is the default language, the document implies that there could be support for other languages in the future, though Python is the primary option at present.

 Can AI Rules Engine be used both at design time and run-time?

Yes, the AI Rules Engine can be used both at design time and run-time. This means it can assist in rule generation during the configuration stage and also execute those rules during the actual workflow.

Does AI Rules Engine use the Azure Open AI Services both at design time and run-time?

Yes, the AI Rules Engine utilizes Azure Open AI Services during both design time (when rules are being created) and run-time (when rules are executed).

What UI Framework is used for implementing the AI Rules Engine UI?

ReactJS - is used for implementing UI for AI Rule Engine.

Where is the AI Python code stored? Is it within the AC Backend Metadata table or in a custom table or somewhere else?

The AI-generated Python code is stored within Adeptia Connect in various metadata tables, including:

• AI_RULE_EXECUTIONS

• AI_RULE_EXECUTIONS_ARCHIVE

• AI_RULES

• AI_RULESET

• AI_RULESET_EXECUTIONS

• AI_RULESET_EXECUTIONS_ARCHIVE

• AI_RULESET_RULES

Where is the AI Rules Engine Python code executed? Is it executed within AC or within Azure Open AI Service?

The AI Rules Engine Python code can be executed either within Adeptia Connect (AC) or using the Azure Open AI Service, depending on the specific implementation.

Where does the code reside once the code is generated? Does it reside within Adeptia or in an external cloud?

Once generated, the code resides within Adeptia. However, depending on the architecture and deployment, it could also reside in an external cloud (e.g., Azure).

What type of file format is supported by Rules Engine for Rule Data input and Rule Data output schema structure? Is JSON the only format supported as AI Rules Engine Input and Output, or are there other input and output schema formats as well?

JSON is the primary format supported for input and output schema in the AI Rules Engine, but there may be support for other formats as well, depending on the specific configuration.

What is the AI Rules Engine Live Option?

The AI Rules Engine Live Option refers to its ability to operate in real-time, dynamically generating and executing rules based on live data during workflow execution.

 Can the Inbuilt AI Rules Engine be Extended with Custom Extensions?

Yes, the AI Rules Engine can be extended with custom extensions, allowing users to modify or add functionalities according to their specific requirements.

How is the AI Rule Engine invoked? (REST Webservices)

The AI Rules Engine can be invoked via REST Webservices, enabling external applications or processes to interact with the engine programmatically.

Is there a feature to create a copy of an existing rule set and save it in a different name?

Yes, there is a feature that allows users to create a copy of an existing rule set and save it under a different name for reuse or modification.

Do all types of Adeptia users have access to AI Rules Engine?

No, not all types of Adeptia users have access to the AI Rules Engine. Access is typically restricted based on user roles and permissions. Specific user roles, such as administrators or users with special privileges, may have access to the AI Rules Engine, while other users may have limited or no access.

Does GAC apply to the AI Rules Engine as well?

Yes, GAC (Global Access Control) applies to the AI Rules Engine. This means that access to the AI Rules Engine and its features is governed by Adeptia's access control policies, ensuring that only authorized users can interact with it.

 Is customer data passed to the AI Rules Engine during the Design Time?

During design time, customer data may or may not be passed to the AI Rules Engine, depending on the use case. Typically, sample data or schema definitions may be provided for rule creation, but it is possible to limit the exposure of actual customer data during this phase.

 Is customer data passed to the AI Rules Engine during the Run-Time?

Yes, during run-time, customer data is passed to the AI Rules Engine to execute the rules and generate outputs. This is necessary for the engine to process and apply the rules on real-time data transactions.

What authentication method is used for calling Azure Open AI Services? (Tip: OAUTH)

The OAuth authentication method is used for calling Azure OpenAI Services. OAuth provides a secure and standardized method for authentication and authorization when interacting with external services like Azure OpenAI.

Who from the customer teams will use the AI Rules Engine?

The AI Rules Engine will primarily be used by technical teams such as data engineers, system integrators, and developers responsible for configuring and managing workflows, automating rule-based decision-making, and ensuring the smooth execution of data processes.

What skill sets are required to use the AI Rules Engine? Do I need to be an expert in AI, Python, ReactJS, or any other technologies?

Users of the AI Rules Engine do not need to be experts in AI, Python, or ReactJS. The primary skills required are related to understanding the data workflows, business logic, and rule generation within Adeptia. However, some familiarity with Python may help in customizing rules if needed, but it is not mandatory.

Who will need to generate the schema definition and the sample data to pass to the AI Rules Engine to design and implement the Rules?

Data engineers or system integrators will be responsible for generating the schema definitions and providing sample data. This information is necessary for designing and implementing rules within the AI Rules Engine.

How can I implement nested AI Rules calls?

Nested AI Rules calls can be implemented by structuring the rules hierarchically within the Adeptia environment, where one rule can call another rule, creating a chain of dependencies. The details on how to configure this would depend on the specific workflow design.

How do I relate my data/record Identifier to the results?

You can relate your data or record identifier to the results by mapping the identifiers within the rule execution logic. The engine allows you to track specific identifiers through the data processing flow and link them to the rule outcomes.

Can I call the AI Rules Engine from the Mapper?

Yes, the AI Rules Engine can be invoked from the Mapper. This allows you to integrate rule-based decision-making directly into the data transformation process handled by the Mapper.

 Is the AI Rules Output return only TRUE or FALSE as result, or can I include my own custom results as well?

The AI Rules Engine can return custom results in addition to simple TRUE or FALSE outcomes. You can configure the engine to provide more detailed outputs based on the rule conditions.

Can I generate a standard Error Code or Error Message as a result across the entire AC Solution Implementation to maintain consistency?

Yes, you can generate standard error codes or messages across the Adeptia Connect (AC) solution to maintain consistency. This is useful for creating a unified approach to error handling across different workflows and rules.

Does the AI Rules Engine support only Flat structure or also Hierarchical structure?

The AI Rules Engine supports both flat and hierarchical structures. This flexibility allows it to handle complex data models where records may be nested or layered within the dataset.

How do I indicate which record or field needs to be tested in a Hierarchical Structure?

In a hierarchical structure, you can indicate the specific record or field to be tested by specifying the node or path within the rule definition. The engine will interpret the hierarchy and apply the rule accordingly to the designated field.

What are the types of Errors that could occur in AI Rules Engine during Design-Time?

Design-time errors in the AI Rules Engine can include:

• Incorrect or incomplete rule definitions.

• Schema mismatches between input data and rule logic.

• Validation errors when defining nested or complex rules.

• Syntax errors in custom rule configurations.

What are the types of Errors that could occur in AI Rules Engine during Run-Time?

Run-time errors in the AI Rules Engine may involve:

• Incorrect data format or structure being passed to the engine.

• Failure to execute rules due to external dependencies or API errors.

• Logic errors where rules return unexpected or incorrect results.

• Performance issues when processing large datasets with complex rules.

Is it possible to have an audit trail of Rules set changes after the Rules have been published for governance of the rule life cycle?

Yes, it is possible to have an audit trail of rule set changes after the rules have been published. This feature is essential for governance of the rule life cycle, allowing users to track changes, modifications, or updates made to the rules over time. It ensures that all changes can be reviewed, making it useful for auditing and compliance purposes.

Is there a plan to implement Rule versioning? It would be a desirable feature to roll-back or go to a previous version.

Yes, there is a plan to implement rule versioning. This feature would allow users to create different versions of a rule set, providing the ability to roll back or revert to a previous version if necessary. Versioning is important for managing changes over time, ensuring stability, and allowing users to restore older versions of rules in case of errors or new requirements.

What is the Accuracy of AI Rules Engine?

The accuracy of the AI Rules Engine depends on several factors, including the quality of the input data, the complexity of the rules, and how well the AI has been trained to handle specific use cases. The document does not specify a fixed accuracy rate, but it implies that the engine can be highly accurate when configured properly, especially in scenarios involving complex data processing where AI-driven decision-making adds value.

How do we test AI Rules Engine during the Design Time?

During design time, you can test the AI Rules Engine by:

• Providing sample data: Using schema definitions and sample data to simulate real-world inputs.

• Validating rule outputs: Running the rules with sample inputs to check if the engine generates the expected results.

• Adjusting and fine-tuning: Based on the test outputs, you can modify the rules or input configurations to improve accuracy and performance.

Using the built-in validation tools: Adeptia's platform likely offers debugging tools that allow you to check if the rules are correctly designed before they go live.

 How do we debug AI Rules Engine execution results during the Run-Time?

To debug AI Rules Engine execution results during run-time, you can:

• Review execution logs: The engine provides execution logs that detail the rule processing steps. These logs can help identify where a rule might have failed or produced unexpected results.

• Check error messages: If the rule fails, detailed error messages or codes can help pinpoint the issue, such as invalid data formats, rule misconfigurations, or integration failures.

• Step through the execution flow: In some environments, you may be able to step through the rule execution flow to observe how data is being processed and how decisions are made at each step.

• Adjust rule logic or input data: Based on the logs and errors, you can modify either the rule logic or the input data schema to resolve issues.

Is the Migration Utility also used for Migrating AI Rules Engine code?

Yes, the Migration Utility can also be used for migrating AI Rules Engine code. This tool is part of the broader Adeptia Connect (AC) platform and is designed to facilitate the migration of various components, including rules, configurations, and code, between different environments (e.g., development, testing, and production).

How does the migration process work for the AI Rules Engine code?

The migration process for the AI Rules Engine code typically involves the following steps:

• Exporting the rule sets: The rules and associated code are exported from the current environment (e.g., development) using the Migration Utility. This process packages the rules and any related metadata into a transferable format.

• Importing into the target environment: Once exported, the package is imported into the target environment (e.g., production) using the Migration Utility. The system ensures that all rule logic, code, and dependencies are properly transferred and configured.

• Validating the migration: After migration, it is essential to test and validate that the rules are functioning correctly in the new environment. This may include running test cases to ensure the integrity of the rules and their outputs.

• Handling version control: The migration process may also involve handling version control to ensure that the correct version of the rules is migrated and that any previous versions are archived or documented for reference.

How does versioning work for the AI Rules Engine code?

Versioning in the AI Rules Engine involves maintaining different versions of the rules and associated code over time. Each time a rule is updated or modified, a new version can be created. This allows users to:

• Track changes to the rules and code.

• Roll back to a previous version if necessary.

• Maintain a history of rule changes for governance and auditing purposes. Rule versioning ensures that the development and updates to rules are properly managed and that older versions are archived for reference or re-implementation.

Where are the AI Rules Engine execution logs stored?

The execution logs for the AI Rules Engine are stored within the Adeptia Connect (AC) platform, typically in backend log tables. These could include tables specifically designed to track rule executions, such as:

• AI_RULE_EXECUTIONS

• AI_RULE_EXECUTIONS_ARCHIVE

• These tables store the details of each rule's execution, providing a history of rule processing activities.

How are the AI Rules Engine execution logs visible to the users?

The execution logs are made visible to users through the Adeptia user interface. Users with the appropriate permissions can:

• Access the logs through a dedicated logging or monitoring dashboard within Adeptia.

• View the details of each rule execution, such as start and end times, outcomes, errors, and performance metrics.

Use filters and search capabilities to locate specific logs related to particular rule executions or error events.

How does the AI Rules Engine log maintenance fit into the Log maintenance policy within Adeptia?

The AI Rules Engine log maintenance is integrated into the broader log maintenance policy within Adeptia. This policy typically involves:

• Retention periods: Logs are stored for a defined period based on the organization's data retention policy. After this period, logs may be archived or purged.

• Archiving logs: Important execution logs (e.g., from AI_RULE_EXECUTIONS) can be archived into tables like AI_RULE_EXECUTIONS_ARCHIVE, allowing for long-term storage and compliance with governance requirements.

• Automated log management: Adeptia may have automated processes for log cleanup and archival to ensure that log storage does not affect system performance while maintaining historical records for auditing and troubleshooting.

What volume of data can AI Rules Engine support?

The volume of data an AI Rules Engine can support varies widely based on the specific engine and its implementation. Generally, it depends on factors such as:

1. Scalability: Some engines are designed to handle large volumes of data by scaling horizontally (adding more servers) or vertically (upgrading existing hardware).

2. Performance Optimization: Efficient data processing techniques, such as indexing, caching, and parallel processing, can affect how much data the engine can handle.

3. Configuration and Resources: The underlying infrastructure, including server capacity, memory, and processing power, plays a crucial role in determining data volume capacity.

4. Use Case and Complexity: The complexity of the rules being applied and the nature of the data (e.g., structured vs. unstructured) can also impact the performance.

What is the alternative traditional way of implementing the rules?

• Hard-Coded Rules: Implementing business rules directly in the application code using conditional statements (e.g., if-else, switch cases).

• Database Triggers and Stored Procedures: Enforcing rules at the database level using triggers or stored procedures to automatically execute actions based on data changes.

• Business Logic Layer (BLL): Incorporating rules within the business logic layer of an application, which processes and applies business rules before storing or presenting data.

• Using Regex in Data Mappers: Incorporating rules using regex for the rules following some specific formats.

What is the advantage of an AI Rules Engine over traditional methods?

• Flexibility and Adaptability: AI Rules Engines can easily adapt to new or changing rules without modifying code, whereas traditional methods require code changes or database modifications.

• Scalability: AI Rules Engines efficiently handle large volumes of data with advanced processing capabilities, while traditional methods may require significant rework for scaling.

• Maintainability: Centralized management of rules in AI Rules Engines simplifies maintenance and updates compared to hard-coded rules.

• Complexity Handling: AI Rules Engines manage complex and dynamic rules with AI and machine learning, while traditional methods can lead to convoluted and error-prone code.

When should you use an AI Rules Engine, and when should you not?

• When to Use:

  • Handling complex, dynamic, or frequently changing rules.

  • Systems requiring high performance and scalability.

  • Scenarios where AI-driven insights or adjustments are beneficial.

  • Need for centralized rule management and easy updates.

• When Not to Use:

  • For simple, static rules that don’t change often.

  • When the overhead of integrating and maintaining an AI engine is not justified.

  • Limited support or expertise for the specific AI Rules Engine available.

What are the design best practices for using an AI Rules Engine as part of the solution?

• Define Clear Objectives: Understand and document the goals and requirements for the AI Rules Engine.

• Modular Rule Design: Break down rules into manageable modules for easier management and updates.

• Continuous Updates and Training: Regularly update rules and train AI models with new data to ensure relevance.

• Thorough Testing: Test rules in a staging environment before deploying to production.

• Monitor Performance: Continuously monitor the engine’s performance and make necessary adjustments.

 What are the absolute Dos and Don’ts of using an AI Rules Engine?

DO’s

• Do ensure data quality: High-quality data is crucial for effective rule execution.

• Do monitor and review: Regularly review and monitor the performance and accuracy of the AI engine.

• Do maintain detailed documentation: Document rules, logic, and configurations comprehensively.

• Do have fallback mechanisms: Implement backup processes in case the AI engine encounters issues.

Don’ts:

• Don’t neglect security: Protect sensitive data and rule logic with robust security measures.

• Don’t ignore user feedback: Use feedback to refine and improve rules.

• Don’t deploy untested rules: Ensure all rules are thoroughly tested before deployment.

What are the best practices design considerations while using an AI Rules Engine?

• Scalability: Design the engine to scale with data volume and processing needs.

• Integration: Ensure seamless integration with existing systems and data sources.

• User Interface: Provide an intuitive interface for rule management and monitoring.

• Error Handling: Implement comprehensive error handling and logging mechanisms.

• Documentation: Maintain detailed documentation of rule definitions, logic, and configurations.

What is the best approach for implementing an AI Rules Engine for a large volume of records within a single file?

• Batch Processing: Divide large datasets into smaller batches to manage system load and performance.

• Parallel Processing: Use parallel processing to handle multiple records simultaneously.

• Optimized Data Storage: Employ efficient data storage solutions to manage and retrieve large volumes of data.

What are the best approaches to handling AI Rules Engine errors?

• Robust Logging: Implement detailed logging to capture error details for troubleshooting.

• Automated Alerts: Set up automated alerts to notify you immediately when errors occur.

• Fallback Mechanisms: Have alternative processes or rules in place to handle errors gracefully and ensure continuity.

What are the best practices for optimal AI Rules Engine performance?

• Performance Tuning: Regularly tune the engine’s performance settings based on workload and data patterns.

• Resource Allocation: Allocate adequate computing resources (CPU, memory) to handle peak loads effectively.

• Regular Updates: Keep AI models and algorithms updated with the latest improvements and optimizations.

• Monitoring and Analytics: Continuously monitor performance metrics and analyze them to identify and address potential bottlenecks.

What are the license implications of using an AI Rules Engine?

Licensing Models: AI Rules Engines typically come with specific licensing terms that can include:

• Per-User Licensing: Costs are based on the number of users accessing or managing the rules engine.

• Per-Instance Licensing: Costs are based on the number of instances or deployments of the rules engine.

• Per-Transaction or Per-Execution Licensing: Costs are tied to the volume of transactions or executions processed by the rules engine.

• Subscription-Based Licensing: Ongoing costs for access to the rules engine, often with tiered pricing based on features or usage levels.

• Compliance: Ensure compliance with the licensing terms, including usage limits, data handling requirements, and reporting obligations.

• Vendor-Specific Terms: License terms and conditions can vary significantly between vendors, so review the specific licensing agreement provided by the AI Rules Engine provider.

Is the AI Rules Engine available in all Tiers of Adeptia, AC Professional, AC Premier, AC Enterprise?

• Adeptia Licensing Tiers: As of the latest information:

  • Adeptia Connect Professional (AC Professional): Typically includes basic integration features and may not include advanced AI Rules Engine capabilities.

  • Adeptia Connect Premier (AC Premier): Often includes more advanced features, which might include AI or machine learning capabilities depending on the version and package.

  • Adeptia Connect Enterprise (AC Enterprise): Generally offers the full suite of features, including advanced AI Rules Engine functionalities, but specifics can vary.

• Availability: The availability of the AI Rules Engine across different tiers can depend on the version and configuration of the Adeptia product. It is advisable to check with Adeptia directly for the most accurate and up-to-date information on feature availability by tier.

What is the cost model for using an AI Rules Engine? Is there an additional cost? Does the cost vary based on the usage in terms of the number of executions?

• Cost Model:

  • License Fees: There may be a base license fee for using the AI Rules Engine, which could be part of a larger package or subscription.

  • Usage-Based Pricing: Some AI Rules Engines have pricing that varies based on usage metrics such as the number of executions, transactions, or data processed.

  • Tiered Pricing: Costs may be structured in tiers, with higher tiers offering more features or higher usage limits.

• Additional Costs:

  • Add-Ons and Extensions: Additional costs might be incurred for advanced features, add-ons, or higher usage limits.

  • Support and Maintenance: Ongoing costs for support and maintenance might be separate from the base license fee.

• Variation by Usage:

  • Per-Execution Costs: Some models charge based on the volume of executions or transactions, so higher usage can lead to increased costs.

  • Scalability: Costs might also vary based on scalability options and resource usage.

• Consult Vendor: For precise details on cost models and any potential additional fees, it is recommended to consult the vendor’s pricing documentation or sales team directly.

 Do the Rules Work on Field Match or File Match?

• Field Match: Rules can be applied at the field level, meaning you can define rules based on specific fields within a file. For example, you can create a rule to validate a policy ID even if it appears in different hierarchies or formats within the same file.

• File Match: Rules can also operate on entire files or datasets. You can configure rules to be applied to the whole file based on its structure or content.

 Where Are the Rules Stored in Backend?

Storage: Rules are typically stored in a database rather than in a single file. This allows for better management, querying, and updating of rules. The rules are often stored in tables or collections within the backend system.

 For Uploading Definition File/Test File, Is There a File Limit? Any Recommendations?

As of now there is no limit set.

• Recommendations: It’s advisable to check the documentation or consult the vendor for specific file size limits and recommendations. Generally, keeping files within manageable sizes helps ensure performance and stability.

Is It Possible to View a Summary of Rule/Rule Set in English After Saving/Modifying?

Summary in English: Many AI Rules Engines offer features to view or generate summaries of rules in human-readable formats. This typically includes descriptions or explanations of the rules, which can be in plain language (e.g., "Rule to check if policy number is an integer with 4 digits").

Can You Copy and Paste or Upload a Definition File (PDF/Word) Containing Rules?

No, it is mandatory to create rules every time. (explicitly add fields )

Is There a Way to Transform Excel/CSV/TXT to JSON Inside the New GUI to Generate a Definition File?

No, as of now this feature is not available.

 Is It Mandatory to Select Fields from a Drop-Down and Be Literal?

Yes, ,Currently we have to select fields from drop down

When Integrating API Calls with Transaction/PF:

• How Will BU Know Rule/Ruleset ID as Shown in Demo?

• What Are All the Headers/Parameters Required for API Call?

• Rule ID: Typically, the rule or ruleset ID is retrieved from the rules engine’s UI or API responses when defining or managing rules.

• Content-Type: Usually application/json or application/xml depending on the API specification.

• Authentication: API key, OAuth token, or other authentication methods as specified by the API documentation.

 Is the Data Encrypted When Passing Files to the Rules Engine?

Yes, it encrypts while passing files to the rule engine..

 How Long Are the Results of a Rules Execution Stored? Is This Configurable?

Results of the rule engine are temporarily stored until it is processed. currently there is no provision of configuring it.

Where Are the Logs Stored?

Logs Storage: Logs are typically stored in a database or file system designated for logging purposes. Check the documentation for details on where logs are kept.

Is There a Cleanup Job?

No, currently this is not implemented; it will be done based on client requirements.

 Since We May Have to Define Rules Based on File Type and Hierarchy, How Big Can the Rules Engine Grow on Server?

Scalability: The scalability of the rules engine depends on its architecture and server resources. AI Rules Engines are designed to handle significant growth, but specific limits depend on the implementation and infrastructure.

Is It Possible to Integrate This Mapping? Can You Demo That?

Integration: Integration possibilities depend on the rules engine’s capabilities and the available APIs or connectors. A demo can be arranged with the vendor to showcase integration capabilities.

Is the AI Rules Engine a Separate Microservice or Deployed on the Same AI Map Page? Do They Share the Knowledge Base?

Deployment: The AI Rules Engine may be deployed as a separate microservice or integrated within a larger application depending on the architecture. Knowledge bases might be shared or managed separately based on system design.

 Is the Rules GUI and Rules Defined GAC Controlled?

GAC Control: The Global Assembly Cache (GAC) control depends on the application and its configuration. If rules are implemented in .NET or similar environments, GAC control may apply.