Unparalleled features for synthetic data generation

You don’t have to be a data scientist to use the MOSTLY AI Platform (but you certainly can be). Our intuitive web-based UI makes it easy for everyone to create high-quality and privacy-secure synthetic data.

And yes, our platform is just more fun to use 🙂

MOSTLY AI uses its own proprietary algorithms to create synthetic data of the highest accuracy in the industry.

Our synthetic data acts as a seamless drop-in replacement, preserving granularity and insights, ensuring consistent results in analytics and machine learning.

Privacy is essential to what we do at MOSTLY AI. We use original data solely for training generative AI models, ensuring it remains anonymous. Our models learn data patterns without direct re-identification risk.

The platform prevents overfitting and safeguards against outliers. Privacy is our default priority in all data synthesis configurations.

A detailed Data Insights Report shows how well a created Generator captures the patterns of the original data.

Various statistics are calculated including univariate and bivariate distributions, as well as correlations.

The Data Insights Report gives you a 360-degree view of your synthetic data for an easy quality assessment.

Data that contains events over time, such as customer behavior records and transaction data, is notoriously hard to anonymize.

MOSTLY AI supports synthesizing this common and valuable data type with unmatched quality, which is mission-critical for many business applications.

MOSTLY AI works with all kinds of structured data: numerical, categorical, and date-time variables are the most important ones.

Additional generative models exist for text and geolocation data.

The MOSTLY AI Platform understands the relationships between tables in a relational database setting, so you can synthesize complex data structures.

In database systems, preserving referential integrity across tables is crucial for meaningful and useful synthetic data. MOSTLY AI enables you to define and maintain these inter-table connections, ensuring the coherence and utility of the synthesized data, whether it's customer-to-transaction or product-to-inventory relationships.

With our data rebalancing feature, users can adjust variable distributions in order to create synthetic datasets that actually diverge from the original data.

For instance, if one demographic is overrepresented, the platform can enhance others, optimizing data for specific use cases, improving insights, and enabling granular 'what-if' analyses.

It can also help to upsample minority classes in imbalanced datasets to  improve downstream model performance.

Synthetically impute missing data points to fill gaps in your data.

Smart imputation addresses missing values more effectively than traditional methods. It uses our Generative AI to consider contextual relationships and patterns, providing statistically appropriate and contextually relevant imputed values.

This enhances dataset accuracy and coherence, ensuring robust foundations for analyses and models.

Fine-tune how conservatively or creatively the MOSTLY AI Platform generates synthetic data.

The MOSTLY AI Platform integrates seamlessly with existing data storage sources, offering a variety of data connectors for convenience.

These connectors include relational databases (MySQL, PostgreSQL, MariaDB, Oracle, and MS SQL Server), cloud data platforms (Snowflake, Databricks, and BigQuery), as well as cloud buckets in Azure, GCP, and AWS.

Connect to MOSTLY AI via our API to integrate synthetic data generation capabilities into your applications, systems, or processes.

Or use our Python Client to conveniently work out of Python environments such as Jupyter Notebooks.

Especially valuable for organizations with automated processes, workflows or multiple interacting systems, ensuring availability and up-to-date synthetic data for various use cases, enhancing platform versatility within any data ecosystem.

Easily deploy the MOSTLY AI Platform in a scalable cluster environment for efficient resource management, scalability, and fulfilling the highest security requirements.

If no cluster is available, the Platform can be installed via Minicube on a Single VM.

Our AI-grade star schema support applies star schema principles to synthetic data generation. It goes beyond the typical two-table setup by allowing the generation of tables based on a single shared table. The primary objectives are to maintain relationships and correlations between tables within a star schema independently and to enhance the accuracy of your synthetic data.

Each table in such a schema is generated sequentially, building upon the context of the tables that came before it. This process ensures the preservation of relationships and context, ultimately improving the accuracy and effectiveness of your synthetic data.

Nested sequences take data accuracy and quality to the next level, particularly when dealing with linked tables in a standard table relationship schema.

For multi-table setups with a 3-level hierarchy, any correlation between the 3rd level entities and all the 2nd level entities, that link to the same subject, is now retained.