How enterprises can make complex business rules easier to understand, test, and change by extending the platforms that run their processes.
What is decision automation? Decision automation applies explicit business rules to input data to produce an operational outcome, such as an eligibility result, calculated amount, or referral for review. Process automation coordinates the work around that decision. Leapter extends existing workflows with visual, executable Blueprints that teams can inspect, test, and improve.
Open a business process model, and you can follow how work moves through an organization. A claim arrives. Information is collected. Systems are consulted. Tasks are assigned.
Then the process reaches a step such as “Assess eligibility,” “Calculate reimbursement,” or “Approve claim.”
What happens inside that step?
In a common business process automation (BPA) architecture, the workflow does not contain the full decision logic. It calls code that does. A service task invokes a Java component, an application service, or an external API. That implementation evaluates the business rules, returns a result, and the workflow continues.
This is the point where Leapter enters the architecture.
Leapter extends the automation platform with business logic expressed as visual, executable Blueprints. The process continues to coordinate the work. The selected decision logic becomes something domain experts and engineers can inspect, test, and maintain together.
The opportunity begins at an existing handoff: the moment the process calls the logic that determines the outcome.
Decision automation: what happens behind the process step
Consider an insurance claims process.
Collecting the claim, retrieving the policy, obtaining weather information, and notifying the customer are process activities. Determining whether the event meets the coverage conditions, which exclusions apply, or whether the case requires specialist review are decisions.
That is the distinction between process automation and decision automation: coordinating activities versus evaluating facts against the logic that determines an operational outcome. The industry treats these as complementary disciplines; Decision Model and Notation (DMN), for example, was designed to work alongside business process modeling.
In a code-based implementation, the process model might contain one activity called “Assess claim.” Behind that activity, a service could evaluate dozens of conditions and calculations.
The process model tells the business expert where the assessment happens. It does not necessarily show how the assessment works.
This is not inherently a problem with external services or with code. Separating process coordination from decision logic can be a sensible architectural choice. The difficulty arises when the people responsible for the policy cannot readily inspect the implementation behind that service call.
A claims expert may understand every coverage condition yet still need a developer to determine which condition rejected a particular case.
With Leapter, the decision rules are clear
With Leapter, that same expert can open a Blueprint and see how the assessment works.
Instead of being hidden in code, the conditions, calculations, and possible outcomes are clearly laid out in a visual Blueprint, alongside descriptions of what they mean. The expert can review which rules apply, what information they use, and how they lead to a decision.
A Blueprint is not just a diagram explaining the logic. It is the logic the system runs.
The expert can also understand what happened in a particular case. With decision tracing, they can follow the assessment step by step, see the values used, and identify the condition that led to the result.
They can review both how the assessment is designed to work and why it produced a particular outcome.
Domain experts can create the decision logic themselves
Leapter goes beyond making existing rules visible. Domain experts can also create and change the decision logic themselves, with AI assistance.
They describe the rules in plain language: what needs to be checked, which conditions apply, and what should happen in each situation. Leapter’s AI assistant helps turn that description into a Blueprint that they can inspect and refine.
When a policy changes, the expert can describe the required update and review the changes to the logic. They do not have to stop at writing a requirement and asking someone else to translate it into code.
The person who knows the rules can work directly on the rules the system will use.
And they can create the tests, too
Understanding a rule is one thing. Checking that it behaves as intended is another.
In Leapter, domain experts can create test cases themselves or ask AI to help generate them. For each case, they define the information to assess and the result they expect under the policy. Leapter runs the logic and compares the actual result with the expected one.
That means the expert can test a straightforward case, an exception, or a value exactly at a policy threshold. If a result is unexpected, they can follow the decision trace to investigate why.
AI can help suggest tests, but the expert checks that the expected outcomes reflect the intended rules.
They are not just reviewing whether the logic looks right. They can test whether it does what the business needs.
Commercial lending: understand how a credit decision is made
Leapter’s published commercial-lending example shows what this looks like in practice.
Consider a business applying for a loan with a thin profit margin and weak collateral coverage. A credit specialist needs to understand how those factors affect the assessment—not just see the result.
In a Blueprint, the specialist can follow the financial-ratio calculations, see how they are classified under the lender’s policy, and review how they contribute to a credit rating. The rules then map that rating to approval, manual review, or decline.
The expert can see how the assessment works and trace why it produced a particular decision.
And the specialist can build this logic, too. They can start from the credit policy, ask AI to create a Blueprint, and review and refine the result. They can also create tests covering different ratings, rejection conditions, and cases close to a policy threshold.
They are not just reviewing a decision. They can create and test the rules behind it.
Keep the workflow. Put the decision rules in the expert’s hands.
In this approach, Leapter extends the existing automation platform rather than replacing it. The workflow continues to gather information, manage tasks, and communicate outcomes. At the assessment step, it uses the Blueprint to evaluate the rules and then continues with the result.
Engineers maintain the connections to the surrounding systems. Domain experts can create, review, and test the decision logic. Changes still need appropriate review before they are released for live use.
The value is not simply that a decision is automated. It is that the people responsible for the rules can understand how that decision works—and help shape it themselves.
With Leapter, domain experts can see why a decision was made, create the logic behind it, and build the tests that check it.
Explore decision automation with Leapter.
Frequently asked questions about decision automation
What is a Leapter Blueprint?
A Leapter Blueprint lays out business rules, conditions, and calculations visually, alongside descriptions. It is not just documentation: it is the logic the system runs. Domain experts can inspect it and trace how it produces a decision.
Can domain experts create decision logic without writing code?
Yes. With Leapter’s AI assistance, domain experts can describe rules in plain language, create a Blueprint, and review and refine the logic. Engineering still supports connections to the surrounding systems.
Can domain experts create their own tests?
Yes. They can define test cases with input information and expected results, or ask AI to help generate them. Leapter compares actual and expected outcomes, and decision traces help investigate differences.
Does Leapter replace the workflow platform?
Not in the approach described here. The workflow platform continues to coordinate the process. Leapter handles selected decision rules in Blueprints that the workflow uses when it reaches an assessment step.
