Process Mining + Business Process Modeling

Start your Process Intelligence journey today with our...New Desktop 26 Edition FREE!

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Begin your process improvement.

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Process Mining and Process Modeling All in One Platform…Download Now

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mindzie Studio vs. Others

Features Others mindzie Studio
Process Mining Map and analyze your process data. Included Included
Business Process Modeling (BPMN) Generate BPMN maps of your process with ease. Not Available Included
AI Copilot Leverage the power of AI to ask questions and gain insight. Not Available Included
Pre-Built Templates Out of the box pre-built analysis templates. Not Available 20+ Templates Included
Dashboards and KPIs Create visual dashboards and KPIs for key insights. Not Available Included

Have a question? check these answers.

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Is Mindzie Desktop free to use?

Yes. Mindzie Desktop offers a fully featured free edition so you can explore process mining, build models, and analyze your processes at no cost.
Process mining is a data-driven technique that analyzes event logs to visualize, monitor, and improve business processes. Mindzie Desktop provides local, high-performance process mining capabilities so you can analyze workflows securely on your own machine.
Yes. Mindzie Desktop is a locally installed application for Windows, allowing you to run process mining analyses without relying on cloud infrastructure.
Mindzie Desktop is currently available for Windows only. If you are using macOS, please contact support@mindzie.com to request a free Mindzie Cloud tenant, which allows you to use Mindzie’s process mining capabilities directly from your browser on a Mac.
Yes. The free version supports up to 1,000 cases. You can upgrade at any time to remove this limit and analyze unlimited cases.
Yes. You can save your mindzie projects, share them to other desktop users can load them, and even load them into the enterprise or cloud edition down the road so you never lose your work.
Yes. The free edition is fully functional for real-world process mining use cases, making it ideal for evaluation, learning, and smaller datasets.