Our business process modeling blog shares the ideas, examples, and advice enterprise teams need to understand how work really runs and where it can improve.
AI agents fail on enterprise processes because they are given steps, not context. Here are the nine dimensions an agent needs, and where they come from.
Object-centric process mining looks at a process through several related objects at once: a customer, an order, four order lines, two shipments, one invoice.
Evaluating Celonis alternatives in 2026? This guide compares the top 10 options, including mindzie, SAP Signavio, and ARIS Process Mining, on price, implementation timeline, deployment flexibility, and depth of process visibility.
Many enterprise AI pilots never reach measurable impact, and the missing piece is usually context, not the model. This post explains how process intelligence gives AI a living model of how the business actually works, reduces hallucinations, and bridges the gap from experimentation to ROI.
mindzie expands with Alibaba Cloud to support Saudi Arabia’s digital transformation and data sovereignty goals
In 2025 leaders are replacing hindsight dashboards with a process intelligence layer that discovers how work actually flows, monitors it in real time, and gives AI agents the context BI cannot. This post covers the shift, the on premise comeback, and a blueprint you can copy.
mindzie has been recognized as a Major Contender and one of only five Star Performers in the Everest Group Process Mining Products PEAK Matrix Assessment 2025, highlighting rapid growth and innovation in AI driven process intelligence.
How to configure Python on a mindzie studio server: install Python 3.11 or later for all users, add the required numpy and pandas libraries, verify the version, and restart the virtual machine.
A short tutorial on transferring complete projects between mindzie studio desktop, cloud, and on premise environments, covering exporting, backing up, and importing project packages without losing any work.
Data residency laws dictate how and where banks can process customer data, which complicates the use of large language models. This post explains the challenges and how locally hosted LLMs combined with process intelligence let banks adopt AI while staying compliant.
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The mindzie team can show you how the ideas from our process improvement blogs translate into real-world results, bringing together process intelligence, BPMN modeling, and AI-ready operational insight in your business environment.
