Modernize the rules engine. Keep the determinism.
Leapter is the AI-native successor to the legacy decision engine. Describe a rule and AI drafts it as a Blueprint — the business logic itself, made visual and executable. The rule owner reads it, tests it, and approves it. Production runs exactly that version. AI helps you build it. No AI when it runs.
Built after LLMs, not retrofitted
Legacy decision engines are retrofitting AI onto formats designed before LLMs existed. Leapter started on a clean slate: the Blueprint was designed from day one to be generated, read, and refined by AI. A domain expert can describe a rule in plain language and review a working implementation in minutes — without learning a modeling notation, and without a specialist translating every change.
That’s not a feature. It’s the difference between AI that writes code you can’t verify, and AI that writes logic you can.
| Capability | Legacy rules engines | AI-generated app code | Leapter |
|---|---|---|---|
| First draft | A specialist models the rule in vendor notation | An agent writes app code quickly | AI drafts executable visual logic from plain language, policy text, or examples |
| Intent stays with the logic | Intent lives in policy docs, tickets, or analyst knowledge | Intent is separated from the generated code | Intent, rationale, and implementation live together in the Blueprint |
| What the business reviews | Decision tables, models, or explanations | App behavior, generated code, or code summaries | The executable visual logic, with the surrounding specification beside it |
| Questions and explanations | Ask a specialist who understands the model | Ask a developer or an LLM to explain code | Ask the Blueprint: AI answers with the logic, intent, tests, and traces in context |
| Test creation | IT-owned test suites and release processes | Developer tests, often detached from business intent | AI-generated sample cases and edge-branch tests tied to the visual logic |
| Change confidence | Slow but controlled | Fast, but hard for the business to verify | Domain experts iterate with AI support, inspect visible changes, run tests, and approve |
| Production behavior | Deterministic rule runtime | Depends on the generated implementation | Approved logic runs deterministically; no LLM in the live decision |
| Audit evidence | Available, but often in specialist tooling | Split across app, code, logs, and test tools | Version, intent, tests, approval, inputs, outputs, and trace tied to the logic |
If you already run a rules engine
You already have determinism — that is the part your engine got right. What Leapter adds is everything around it:
AI drafting from your policy documents. Drop in the credit policy, underwriting guideline, or pricing directive. Leapter drafts the decision logic from the document — every condition, every branch, every threshold laid out for review — instead of weeks of rule modeling.
Diagrams the expert approves directly. The rule owner reads the logic as a diagram, changes a threshold, reruns the tests, and signs off. No developer queue between the policy and the rule.
Generated tests and a trace for every run. Leapter generates test cases for branches and edge cases before approval, and every production run records which branches fired and why — evidence you can hand to an auditor without reverse-engineering anything.
What it does not replace: the systems around the decision. Data integration, case management, and the applications that call your rules stay where they are — they call Leapter the way they call your engine today. And you migrate one rule set at a time, not in one cutover.
From policy document to running rule
01. Co-author the logic
Describe the rule in plain language, or drop in an existing policy document. Leapter turns your description into a Blueprint. Every condition, every branch, every threshold laid out.
02. Review and refine
See how each decision is made. Change a threshold, reorder a branch, adjust the logic directly. No code to read. No developer queue.
03. Deploy with deterministic execution
The approved Blueprint is exactly what runs. Same input, same output. No LLM in the decision path.
Use cases
Credit decisioning
Risk teams maintain traceable decision flows that support model risk management reviews. Every approved or declined decision links to a Blueprint version a named human signed off.
KYC and compliance checks
Compliance teams can turn policies, thresholds, and review triggers into visual decision flows. The goal is not a compliance guarantee; it is evidence the team can read when automated decisions come under scrutiny.
Insurance underwriting
Underwriters define eligibility, exclusions, and referral triggers in their own language — logic an underwriter can read on a single screen and change the morning a guideline changes, not after the next release.
Pricing and quote rules
Pricing leads update rate factors and quote rules without a developer ticket. Every quote ties back to the exact Blueprint version that produced it — for the customer, the auditor, and the regulator.
Eligibility & benefits
Policy owners encode regulations into Blueprints they update the day the rule changes. No translation gap between the published policy and the deployed logic.
Bring one rule set. We’ll show you what it looks like as a Blueprint.
What changes day to day
Spec view
Read the whole project as a structured document with diagrams in context, or zoom into a single rule. The review happens around the logic itself, not around a ticket or a demo.
Decision trace & step replay
Every execution produces a complete record of which branches fired. Walk through any run one step at a time and see exactly which branch fired and why.
Test & audit
Test decision branches and edge cases with automatically generated test cases. Every live execution is logged with a full trace and can be re-run with one click.
The full product walkthrough — what a Blueprint is, the four-step workflow, the feature inventory — lives on the Product page.
Where it runs
The first question every risk-technology evaluation asks, answered plainly:
- Deployment: SaaS, private cloud, or on-premises.
- What the drafting AI sees: Only design-time prompt inputs are transmitted to the LLM. Approved Blueprints run deterministically without an LLM in the live decision.
- Traces: Every execution can be traced back to the approved Blueprint version, inputs, outputs, and branches that fired.
- Portability: Blueprints can be called via REST API or MCP, or exported to JavaScript/Python when teams need to embed the logic directly.
Built for the people who own the rules
Rule owners — credit risk, underwriting, pricing, policy
The rules are your accountability and your expertise. Build and change decision logic in your own language, test the cases you worry about, and approve exactly what runs — without a ticket for every threshold change.
Risk technology & engineering
Stop translating policy changes into code and explaining the engine to auditors. Give rule owners a governed authoring layer, and keep a stable, versioned API for every system that calls the rules.
Leapter is built by founders with decades of experience with visual business rules and regulated decisioning — they have spent careers on the kind of system you are thinking about replacing.
For enterprise teams
Book a 30-minute briefing. Bring one example from your business: a decision, rule set, workflow, calculation, or policy. We’ll show how it becomes visual logic your experts can read, test, and approve.
- Map your real example
- Review and test the visual logic
- Assess design-partner fit
For investors
Request the investor deck and technical brief covering market size, EU AI Act tailwinds, product architecture, and the design-partner path to traction.
- Market and EU AI Act thesis
- Product and technical brief
- Go-to-market and design-partner pipeline
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Contact us at any time through info@leapter.com
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