Governed Decision Automation for Applications and AI Workflows

Define, test, approve, execute and explain the business decisions that drive your .NET applications and AI-powered workflows.

Code Effects V7 combines deterministic rule execution with AI-assisted rule authoring, decision management, production auditing and AI agent integration - while keeping your organization in control of data, infrastructure and production changes.

Code Effects goes Java!

Our entire decision automation platform is coming to one of the world's most widely used programming languages. Current ETA: Q1 2027.
Subscribe to our product announcements to be notified when it arrives.

Code Effects Repository Rule Version Details Stats Tab Code Effects Repository AI Rule Editor Code Effects Rule Repository Version Test Code Effects Rule Evaluation Result Dialog
Code Effects Decision Automation Platform 

Why Code Effects?

Complete Rule Management

Bring rule authoring, versions, drafts, tests and approvals into your application with the Rule Repository. Give business users a ready-made workspace backed by a database you control.

AI-Assisted Rule Authoring

Create and refine rules through conversation with the AI Rule Editor, then inspect and adjust them in the Standard Rule Editor. Generated logic is checked against your application’s source objects.

Test Before Going Live

Explore what-if scenarios, define expected outcomes and run saved tests in batches. Inspect the decision path and resulting values before approving a change.

People Control Production

Immutable versions, explicit approvals and application-defined permissions control what reaches production. AI agents can prepare and test changes; approval and publishing remain human responsibilities.

Explain Live Decisions

With Audit, investigate the exact rule version, recorded values and decision path behind a production outcome. Turn a recorded evaluation into a test using its actual inputs.

Connect Your AI Agents

Let compatible external AI clients work with rules, tests and recorded decisions through the MCP server, using your application’s authentication and permissions.

Your Infrastructure and Models

Run Code Effects within your environment and choose the models and API keys used by its AI features. The Repository’s built-in AI tools work without the MCP server.

Predictable Perpetual Licensing

Execute rules directly within your .NET applications. Choose a perpetual commercial package with no recurring subscription or per-evaluation fees.

AI-Assisted Rules.
Explainable Decisions.
Human Control.

Bring AI into the way your team creates business logic, investigates decisions and understands production results. Code Effects v7 combines conversational rule authoring with an AI Assistant that works across your rule environment. Choose your own tool-calling models and API keys, while your application controls access and authorized people decide what reaches production.

LLM Architecture

Describe and Refine Your Rules

Describe the business logic you need in any language, or ask AI to change an existing rule. The AI Rule Editor builds the rule from the fields and operations your application exposes, then checks its structure and compatibility with the source object. Switch between conversational authoring and the standard Rule Editor to inspect and refine the result. Explore alternatives as drafts, test different inputs and verify expected outcomes before submitting a version for approval.

AI Prompt

Investigate Real Decisions

Ask the AI Assistant to find and explain rules, examine test history, summarize user activity or calculate aggregates from available records. With Audit data, investigate why a particular decision occurred and explore patterns across production evaluations. Upload PDF policy documents to compare written requirements with existing logic. Move from “Why was this application declined?” to the relevant rule version, recorded inputs and conditions behind the outcome—all within the user’s permissions.

Deterministic Results

Connect AI Under Your Control

Use the Repository’s built-in AI Rule Editor and AI Assistant without an MCP server, or add MCP to connect compatible external AI clients through your application’s authentication. Agents can prepare rules, run tests and investigate available history within enforced permissions. They cannot approve, publish, delete or take rules offline. Saved rule versions require human approval, preserving accountability while AI helps your team move faster.

Our Customers

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  • Ernst and Young Logo
  • Equifax Logo
  • DuPont Logo
  • Husky Logo
  • PWC Logo
  • Verizon Logo
  • MetLife Logo
  • Moodys Logo
  • Aon Logo
  • MassMutual Logo
  • New York Life Logo
  • Humana Logo
  • Chevron Logo
  • Barclays Logo
  • The Home Depot Logo
  • Ernst and Young Logo
  • Equifax Logo
  • DuPont Logo
  • Husky Logo
  • PWC Logo
  • Verizon Logo
  • MetLife Logo
  • Moodys Logo
  • Aon Logo
  • MassMutual Logo
  • New York Life Logo
  • Humana Logo

Governance

Keep business logic under your organization’s control throughout its lifecycle. V7 brings immutable rule versions, testing, approvals and controlled publishing into one workspace. Your application determines who can perform each action, while activity records preserve who changed what and when. AI agents can prepare and test proposed changes; authorized people remain responsible for approval and deployment.

Intelligence

Turn business requirements into executable rules through conversation. The AI Rule Editor helps users create and refine logic in any language, while the AI Assistant finds rules, runs tests and answers questions across available history, recorded decisions and policy documents. Choose your own tool-calling models and API keys, and connect external AI clients through the optional MCP server.

Explainability

Understand how your business logic produces each outcome. Tests reveal the decision path, relevant conditions and resulting values. With Audit, production records connect decisions to the exact rule versions and recorded inputs behind them. Investigate individual outcomes, explore production statistics and turn recorded evaluations into repeatable tests, giving teams evidence to review decisions and improve their rules.

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