Discover what Mostly AI is and how to use it effectively in 2025. Explore its features and see how it stacks up against other Data Analytics Tools.

Founded in Vienna, Austria, back in 2017, Mostly AI is a company built by data scientists Michael Platzer, Klaudius Kalcher, and Roland Boubela. They’ve really focused on using AI to create synthetic data. What does that mean? Well, they offer services like generating high-quality synthetic data, making sure data privacy and security are top-notch, providing professional training, using this synthetic data to develop AI and machine learning models, and even making it possible to share data across borders safely. Their platform, called GenAI, is pretty neat – it lets you explore and share tabular data, helps make AI and ML fairer and less biased, and makes self-service analytics a breeze. Ultimately, Mostly AI wants to change how companies handle data, pushing for smart, safe synthetic data to build a better, fairer future for everyone.
Mostly AI got its start in 2017 in Vienna, Austria, thanks to its founders: Michael Platzer, Klaudius Kalcher, and Roland Boubela. These guys are all distinguished data scientists who saw the huge potential AI held for generating structured business data and, specifically, synthetic data. They were particularly inspired by the data anonymization hurdles companies faced, especially with GDPR rolling out in 2018. The core mission at Mostly AI is to help organizations thrive ethically by using safe synthetic data. Their big goal? To empower everyone with data and build a smarter, fairer future.
Mostly AI is a great tool for a wide range of professionals:
Getting started with Mostly AI is pretty straightforward. Here’s a quick rundown:
To dive in, you can start by playing around with the sample datasets they provide. Or, if you prefer, you can upload your own datasets to create synthetic versions.
Mostly AI’s AI-powered technology is there to help you generate high-quality, privacy-safe synthetic datasets for all sorts of uses.
Need to share data across borders without privacy worries? This platform makes it possible.
Plus, you can tackle biases in your training datasets by using their algorithms, which really helps enhance fairness in your AI/ML models.
And don’t forget the handy features like natural language interfaces for exploring your data, a Python client for your data workflows, and the ability to create tabular data from scratch or even enrich datasets you already have.
Following these steps should give you a smooth entry into the world of synthetic data, whether you’re looking at analytics, AI/ML development, product creation, or software testing. And remember, it all happens while keeping your data private and secure.
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