Modeler
GOVERNED DECISION MODELING
InRule Modeler
Design it. Prove it. Govern it. Before it runs.
InRule Modeler is the design workbench of the InRule platform, built around two connected environments: one where business teams and developers create and change decision logic, and one where every change is proven against production reality before it moves through governed review.
Model decisions you can trust
Every regulated decision starts as a change someone has to make. A new rule, a revised threshold, a policy update sitting in a development queue. InRule Modeler is where your business teams make the change, tested against existing decision logic, and ship it through governed review, start to finish, without waiting on a release cycle.
Business teams own the change
Your IT teams build the decision foundation in a low code workbench. Then natural language authoring, a shared vocabulary, and decision tables put every change in the hands of the people who own the policy.
Tested before it ships
Test every change with irVerify and irAuthor Web Tester to validate every rule logic before production rollout. Regression suites will test a change against your entire library of test cases at once.
Governed at every step
Every change, whether human written or AI assisted, moves through the same review and approval process, so nothing reaches production ungoverned.
Own the logic behind every decision
When rules live in code, nobody can move fast
When decision logic lives inside application code, business teams cannot change it and developers cannot explain it. InRule Modeler separates rule logic from code, giving business analysts direct access, reducing compliance risk, and eliminating the translation gaps that produce costly errors.
Externalized rules
Rule logic lives outside application code. Business analysts author, test, and update rules directly. When a regulation changes, your policy team makes the change, not the dev team.
Business vocabulary
InRule Modeler uses natural language aligned with your organization’s terminology. Rules read the way your business talks, so analysts and developers work from the same definitions.
Traceability
Every decision path and rule
change is captured. When an auditor asks what rule governed a
decision, and who changed it last, the answer is in the system.
Visual collaboration
Cross functional teams review and align on rule logic together. Visual tools surface what the rules do, not just what the code says.
Rule quality
Simulation and regression testing validate rule logic before deployment. Every change can be tested. against real scenarios before it reaches production.
One governed workbench, start to finish
InRule Modeler is a single governed workbench where you can create, modify, and test your business rules. Model new rules or modify an existing rule using natural language, change a decision table, or code, and test it in the same place, with an AI assistant that works from your real rule structure and never acts without a person’s approval. Every change then moves through the same governed review before it ships.
Model
Describe the change in natural language, edit a decision table, or author it directly, grounded in a shared vocabulary your team understands. Test it in the same workbench to ensure the change does what you intended.
Approve
The change moves through the review and approval process you already have in place. Written by a person or drafted by AI, the gate is the same.
Ship
Approved logic deploys to InRule Decisions as the only version the engine will run. The execution history informs the next change.
The Modeling Environment
Create and modify rules with confidence
Business users, technical analysts, and IT teams author decision logic here, in natural language, in decision tables, or in code, all grounded in a shared business vocabulary. The InRule AI Harness gives this workbench an assistant that understands your actual rule structure and schema. It searches your rules in plain language, explains what a rule does and why, and translates a described change into a structured, testable edit.
Make the change
✓ Search rules using plain language
✓ Get a plain language explanation of any rule’s logic
✓ Describe a change; the AI Harness drafts the structured edit
✓ Generate synthetic test data to validate the edit before it moves forward
Every AI assisted edit still needs a person to accept it. The AI Harness can search, explain, and draft, but nothing it writes becomes part of your rules until someone on your team reviews it and says yes.
The Validation Environment
Test every change against reality
Before a change ships, test it with irVerify and irAuthor Web Tester, built into the same workbench you used to make the change. Define test cases with the inputs and outcomes you expect, run them against your updated logic, and confirm the change does only what you intended. Regression suites will run a change against your entire library of test cases at once, so you catch anything the change might have broken elsewhere, not just the scenario you were focused on.
Prove it works
✓ Define test cases with expected inputs and outcomes, and verify your logic produces them
✓ Catch a broken rule or an unintended side effect before it ever reaches production
✓ Regression suites test a change against your entire test library at once
✓ Results carry directly into the record used for review and approval
Modeling and testing live in the same workbench so a change you write here is the same change you test here, and the results feed straight into the review that approves it.
The Modeling Environment
Create and modify rules with confidence
A business user should not have to leave the tools they already use to get a rule change started. Through MCP, InRule surfaces this assistance in the tools your team works in already, without requiring anyone to open InRule Modeler first. InRule’s MCP surfaces in tools like Copilot, ChatGPT, or any MCP compatible surface.
✓ Search, explain, and propose rule changes from existing AI tools without opening InRule Modeler
✓ Every change still routes through InRule’s governed review and approval process before it takes effect
✓ Policy documents stay in your existing systems; AI works from the governed source, not a copy
A visual trace of every execution for full transparency of every decision flow walks you through any decision, from the first condition to the final outcome, before it ships, to understand what you are approving, and after it runs, to explain what happened. Analysts see the logic in business terms, and auditors see the trace in full.
Aligned to your data model
A schema generator and visual viewer, with strong typing and inline validation
Decision logic is only as good as the data underneath it. Modeler builds the schema straight from your decision model and keeps the two aligned as both evolve, so the data gap that would have broken a decision in production gets caught while it is still a red underline.
Related case studies
From rules to results
Explore how leading enterprises in regulated industries use InRule to move faster, stay compliant, and reduce risk.
