https://www.progress.com/corticon enables organizations to automate complex, explainable business decisions without embedding policy logic in application code.

Domain experts can author, test, and maintain rules using visual modeling tools, while development teams can deploy those decisions across centralized services, cloud platforms, serverless architectures, browsers, mobile applications, and edge environments.

This organization provides sample rule projects, integrations, and demonstrations for both Corticon Classic and Corticon.js.

1. Author rules in https://docs.progress.com/bundle/corticon-quick-reference/page/A-guide-to-Progress-Corticon-Studio.html or https://docs.progress.com/bundle/corticon-js-quick-reference/page/A-guide-to-Progress-Corticon.js-Studio.html.
2. Explore the sample repositories below and import the relevant rule projects into your development environment.
3. Build and deploy a Decision Service using the runtime and deployment architecture appropriate for your application.
4. Integrate decisions through REST or SOAP services, embedded Java or .NET APIs, JavaScript function calls, or cloud-native application interfaces.

Both Corticon Classic and Corticon.js support complex enterprise decisioning. They share the same core objective: enabling organizations to externalize, automate, explain, and maintain business rules independently from application code.

The appropriate runtime depends primarily on the target architecture, integration requirements, operational model, and preferred deployment technology—not on whether the decision is an enterprise use case.

Corticon.js is not limited to user-interface logic or small edge applications. It can be used for centralized enterprise Decision Services, high-volume transaction processing, eligibility and benefits determination, compliance, financial services, insurance, public-sector casework, and other demanding decision workloads.

As with any execution platform, rule-modeling and deployment choices can materially affect performance. Models should be designed and tested using patterns appropriate to the selected runtime, data structures, and expected workload.

Corticon can support decision automation across a broad range of industries and architectures, including:

• Eligibility, benefits, and casework decisions for government and public-sector programs
• Compliance and policy automation with transparent, explainable outcomes
• Underwriting, claims, lending, and fraud-related decisions
• Healthcare, insurance, and financial-services policy administration
• High-volume transactional and batch decision processing
• Centralized Decision Services exposed through APIs
• Cloud-native and serverless decision services
• Dynamic, rules-driven forms that adapt in real time
• Decisions embedded within web, mobile, desktop, and edge applications
• Offline decisions where network access is intermittent or unavailable

The choice between Corticon Classic and Corticon.js should be based on deployment and integration requirements.

• The solution will be managed through the Corticon Server and Web Console platform.
• Deep integration with Java, .NET, or existing Corticon Server infrastructure is required.
• The organization relies on established Corticon Server operational and monitoring practices.
• The application requires Classic-specific server extensions or integration capabilities.

• JavaScript or Node.js is the preferred application runtime.
• The solution requires cloud-native, containerized, or serverless deployment.
• Decision Services need to scale horizontally across multiple runtime instances.
• The same decision logic may run centrally and within browsers, mobile applications, or edge environments.
• Portable, self-contained deployment is important.
• Local, low-latency, or offline decision execution is required.

These are architectural considerations rather than restrictions on the complexity or business importance of the decisions being automated.

Samples for building and deploying Corticon.js Decision Services in JavaScript-based environments. Examples include Node.js, serverless platforms, web applications, JavaScript frameworks, browser execution, extended operators, and service integrations.

Sample rule projects for the Java-based Corticon Classic platform. These projects can be imported into Corticon Studio and deployed to Corticon Server or embedded within Java and .NET applications.

A rules-driven, framework-independent approach to building dynamic forms with Corticon.js. Business rules control form flow, visibility, validation, and behavior separately from presentation code.

An example of executing a Corticon.js Decision Service within MarkLogic, combining business-rule automation with data and document processing in the MarkLogic environment.

Examples for integrating with and extending Corticon Server, including service callouts, Enterprise Data Connector usage, and other Java-based integration patterns.

Resources for capturing and analyzing Corticon Server rule-execution details. These capabilities support decision auditing, execution analysis, operational visibility, and compliance requirements.

Workshop materials demonstrating the use of Corticon for Medicaid-related decision automation and solution development.

Complex enterprise decision models can be implemented with either Corticon Classic or Corticon.js. Because the runtimes use different execution technologies, the most effective implementation pattern may not always be identical across them.

When designing performance-sensitive Decision Services:

• Constrain collection processing as early as practical.
• Avoid unnecessary expansion of associated collections.
• Reuse filtered collections, aliases, and intermediate results where appropriate.
• Evaluate simpler conditions before performing more expensive collection operations.
• Test with representative payload structures, data volumes, and concurrency.
• Evaluate the complete deployed architecture rather than relying only on isolated development tests.

Performance comparisons should use functionally equivalent models that are appropriately designed for each runtime.

• https://docs.progress.com/category/corticon-information-hub
• https://www.progress.com/corticon
• Corticon.js Samples
• Corticon Classic Samples

Contributions that expand the available Decision Service examples, deployment patterns, integrations, rule-modeling guidance, and representative enterprise scenarios are welcome.

When contributing a sample, clearly identify:

• The supported Corticon runtime and version
• The business scenario demonstrated
• Required supporting software or cloud services
• Instructions for building, testing, and running the example
• Any runtime-specific modeling or deployment considerations