Camunda 8 is a powerful process orchestration platform, but when it comes to complex decision logic, its built-in DMN engine can sometimes feel limiting. It supports a subset of the DMN 1.3 standard (Conformance Level 2ish), which is great for simple decision tables but struggles with advanced requirements like:

• Complex FEEL expressions, list handling, and Contexts
• BKMs (Business Knowledge Models) and Boxed Expressions
• Cross-decision dependencies and invocations
• Stateful knowledge (e.g., historical data for KPIs)
• Deep insights into decision execution

Enter QuantumDMN. We provide full DMN 1.5 Conformance Level 3 support, meaning you can run any valid DMN model—including those with functions, list filtering, and complex contexts. Plus, our engine offers unparalleled transparency into how a decision was reached.

In this post, we'll show you how to get the best of both worlds: Camunda 8 for BPMN orchestration and QuantumDMN for advanced decisioning.

In healthcare, timely and accurate decisions save lives and resources. One critical challenge is managing Patient Readmissions. Hospitals are often penalized for excessive readmissions, and more importantly, they represent a failure to fully resolve a patient's condition.

In this post, we'll explore how QuantumDMN uses stateful decisioning (KPIs) to automatically identify patients at risk of readmission without complex backend queries.

In the high-stakes world of Fintech—whether it's real-time payment processing, loan origination, or fraud detection—decisions can rarely be made in isolation. A purely stateless "Yes/No" often isn't enough. You need context. You need history. You need Stateful Decisioning.

In this post, we'll demonstrate how QuantumDMN solves a classic Fintech problem that traditional stateless DMN engines struggle with: Velocity Checks.