Start in ChatGPT, then use Leapter to inspect the rules, trace outcomes and review the logic that runs.
Leapter now gives business experts a new way to begin creating rules they can inspect, verify and develop in Leapter; where the visual logic they review is the logic that runs.
Decision rules are often embedded in code that business experts cannot easily review; Leapter makes the rules themselves visible and executable, so the people responsible for the policy can verify the logic that actually runs.
Whether a company is checking eligibility, calculating a price or deciding when a case needs approval, Leapter makes the conditions, calculations and exceptions behind that decision visible. Experts can examine these rules in an executable visual diagram, called a Blueprint, and check that it reflects the intended policy without reading source code.
The plugin lets users create a Blueprint in ChatGPT and save it to Leapter Lab, the company's workspace for developing decision rules. There, they can refine the logic, test scenarios and follow how an outcome is reached.
Try the Leapter plugin in ChatGPT
Leapter combines AI-assisted rule creation with deterministic execution: the same inputs and deployed rules produce the same result. Applications and AI agents can call that logic. This gives businesses a way to use AI to develop their rules while keeping the decisions themselves reproducible and traceable.
See the rules. Verify the behaviour. Explain the result.
Leapter gives teams three practical ways to examine and control their decision rules:
Make the rules visible and verifiable. Business experts can inspect conditions, thresholds and calculations, test everyday cases and exceptions, and check the results against their intended policy. Their review concerns the actual executable logic.
Trace how an outcome was reached. Recorded production runs show the inputs, result and rule version used. Teams can replay the recorded path through the visual logic to see which conditions led to an outcome, helping them investigate unexpected results and explain individual decisions.
Keep evidence for review and audit. Version history shows what changed, who changed it and when. Review workflows record comments, decisions and approvals, giving teams evidence to examine how their rules evolved and who reviewed them.
A pricing team, for example, could check why one quote received a discount and another did not, identify the thresholds and exceptions involved, and test a proposed policy change before putting it into use.
