The fastest way to create
trusted data

Create high-quality data for machine learning, application development and testing with easy-to-use config files.

Write data requirements in a YAML config file for a database or using Python DSL for a dataset.

Create a data transformation job, check data categorization and access rights.

Run job as part of CI/CD or data pipeline to create data in the destination that meets the requirements.

Specify requirements

Create workflow

Run job

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Create data with Augmedatum to:

Adopt DevOps for your database management

Make your database provisioning cloud-native using our database generation, masking and subsetting engine that runs in Kubernetes.

Why augmedatum

Automate test data management with the power of AI

No more manual configuration of complex data transformations. Use modern AI-driven techniques for data masking and generation.

Facilitate cloud migration

Synthetic high-quality database generation for functional validation, performance, and integration testing in the cloud for Snowflake, GCP, Amazon Redshift, and Microsoft Azure.

Shorten time to data

TDK

Create reusable data transformation workflows to shorten data collection lead time. Iteratively set up and test ML pipelines with Airflow, GCP Cloud Composer, Dbt, Spark, and other ETL tools instead of waiting for the lengthy original data access procedures.

Increase market value of existing data

Generation of high-quality synthetic data for data monetization with third-party firms that preserves statistical properties of original data and is free of sensitive PII/SPI data.

TDK

TDK

SDK

SDK

Automatic data rebalancing, upsampling, bootstrapping for backtesting, and data imputation to increase the performance of machine learning models by up to 15% without changing model structure.

SDK

Improve ML model performance

An API-first approach to create high-quality data

Faster outcomes

Fast & easy deployments

Simple API to integrate into your CI/CD or data pipeline, both on-premise and cloud. Supports all relational databases and data governance platforms (BigID, Collibra, Zaloni) and deployments using Kubernetes, OpenShift, and Docker.

Improved data quality

Our machine learning models learn statistical properties in a table and across tables to help create high-quality data, often better than production data.

Full automation

DevOps-friendly API-driven framework to create tabular data and databases in minutes using YAML config files and Python DSL.

Guaranteed compliance

"Data as Code" approach enables you to codify complex compliance requirements into concrete data transformations.

Create production-like data in your cloud tenant

Full integrations

Deploy instantly, supercharge effortlessly, and accelerate initiatives with seamless cloud marketplace integrations. “Data as Code” approach makes it easy for anyone to be a data engineer.

Whether you are a data engineer, data scientist, or machine learning researcher, Augmedatum SDK can be easily integrated into your existing workflows for ETL, data preparation, and model training. It's all set and ready to use.

Cloud

Cloud Dataproc

AWS

Run our products on AWS.

GCP

Run our products on GCP.

Microsoft Azure

Run our products on Microsoft Azure.

Our products

Create a generative model
for any dataset

Apply Augmedatum Scientific Data Kit (SDK) to bootstrap data where the density of data is low, automatically rebalance data to improve model performance, and anonymise data for repurposing.

Benefit from up to 15% uplift in model performance with data rebalancing, data imputation, and high-quality synthetic data generation. SDK helps increase revenue across conversion, fraud, revenue recovery, and more.

Improved model performance

Extend and plug-in into any data platform or ETL pipeline including Airflow, Dataproc, Spark. Fast and easy deployments using Kubernetes, OpenShift, and Docker.

API-first extensible framework

"Data as Code" approach enables you to codify complex compliance requirements into concrete data transformations.

Guaranteed
compliance

SDK

Full visibility of key data metrics including data quality, data compliance, and model performance metrics in your reports.

Full analytics and reporting

100+

~4-15%

<10 mins

#1

Model performance
improvement via
automated data quality

Automated data compliance framework

To bootstrap data or
rebalance the
underlying dataset

Supported ML models
and use cases

Quality automation

Rebalancing

Compliance automation

Automate high-quality data creation using machine learning and common workflows.

Amplify the signal and reduce noise from original data. Multiple scenarios that allow for thorough model testing.

Automatic data upsampling and bootstrapping for backtesting, cross-validation and more. Unlimited volumes of data.

Codifying data compliance requirements into concrete data transformations.

Generate required volumes of anonymous data from generative models.

Robustness against complex attacks, such as linkage attacks and attribute disclosure.

Configure data masking parameters 
to meet your organization’s needs.

Data compliance requirements verification 
and validation.

Data

It’s all about

Bootstrapping

Augmedatum delivers the fastest way to create and share trusted data

The inception of Augmedatum originates from a shared vision of Dmitry Bragin and Alexey Khromov, both hailing from diverse backgrounds in technology and real estate. Their combined expertise set the foundation for our venture. Our goal is to bring an innovative approach to the generation and processing of synthetic data, which would led to groundbreaking changes in machine learning and data science.

Augmedatum's journey began at a technology workshop in 2022, where Dmitry and Alexey, along with Alesia Dubrouskaya and Mikita Damashevich discovered a mutual passion for solving the modern problems of data usage in the field of artificial intelligence. This meeting quickly transitioned into action, with the founding of Augmedatum in March 2023. Identifying the critical gap in the availability of relevant and privacy-compliant data for AI development, the team embarked on developing a suite of tools that stand at the core of Augmedatum’s offerings: the AugDataGen SDK and the AugDataTest TDK.

The company's development phases reflect a meticulous approach to innovation, from conducting in-depth market research to prototyping a platform capable of generating quality synthetic data.
Looking ahead, Augmedatum is poised to broaden its technological horizons, driving forward the fields of AI and ML with innovative, secure, and compliant synthetic data solutions. Our strategy involves a continuous loop of refinement and expansion, underscored by a significant investments in R&D. Our key technologies are ready and products are being tested, already bringing positive results to our customers. Learn more about our our offerings and exclusive capabilities by contacting our sales department!

Our story

About us

Founders

Khromov
Alexey

Chief Technology Officer

Bragin
Dmitry

Chief Executive Officer

Dubrouskaya
Alesia

Chief Marketing Officer

Damashevich
Nikita

Chief Product Officer

Svetlana Korneva

Chief Financial Officer

Don't see the role you're interested in?

We’re always looking for talented people to join our team. Send us your CV and we will contact you for any future roles.

Contact Sales

• Easy onboarding

• Ongoing support

• Hassle free

Start a conversation with us today about how Augmedatum can help you solve your data challenges.

After you submit your request, we’ll get in touch with you shortly to suggest a time for an introductory call.

Our promise

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