Syntho

Data Management

Syntho

Unlock data potential with Syntho's AI-generated synthetic data.

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About Syntho

Syntho is an innovative Amsterdam-based company specializing in AI-generated synthetic data, designed to help businesses overcome data limitations and unlock new opportunities. The core purpose of Syntho is to enable organizations to generate high-quality synthetic data that replicates the statistical properties of real data without compromising privacy. This approach addresses challenges linked with data privacy regulations and fulfills the demand for large datasets essential for various applications. Syntho provides three key solutions: AI-generated synthetic data for analytics and AI modeling, smart de-identification of sensitive information to comply with data privacy regulations like GDPR, and test data management that supports development cycles without using real production data. Its Syntho Engine offers robust capabilities, including automated smart de-identification, time series data synthesis, upsampling, consistent relational database mapping, rule-based data generation, and AI-powered PII scanning—all delivering privacy and data utility. These capabilities extend Syntho's utility across industries, influencing AI modeling, analytics, data sharing, and data monetization. Unique selling points of Syntho include its integrated solution combining AI-generated data, de-identification, and test data management on one platform, high accuracy in data generation, a privacy-centric design, and a user-friendly interface that integrates seamlessly with other systems via REST API. The Syntho Engine supports various environments such as on-premise, private clouds, and Syntho’s own cloud infrastructure, deployable via Docker-Compose and Kubernetes. Recognized for its groundbreaking work, Syntho has been honored with several awards, including the Philips Innovation Award, UNESCO’s Challenge at VivaTech, and being noted as a generative AI startup by NVIDIA. Noteworthy too is their achievement as winners in the global SAS Hackathon for Healthcare and Life Sciences. A recent development includes a significant partnership with the biobank Lifelines, enhancing data access for research while safeguarding privacy, thereby affirming Syntho's expanding influence in the synthetic data landscape.

Key Features

  • AI-generated synthetic data that reflects real data patterns without containing sensitive information
  • Smart de-identification features automatically remove or modify Personally Identifiable Information (PII) for privacy compliance
  • Comprehensive test data management that safeguards sensitive information while supporting software development and testing cycles
  • The Syntho Engine supports various data types, ensuring ease of deployment and integration
  • Accurate synthesis of time-series data for time-dependent analyses
  • Upsampling to address data imbalances and improve data representativeness for machine learning model training
  • Full data coverage with optimized support for complex data structures
  • High accuracy of synthetic data, approved by experts at SAS

Tags

AI-generated synthetic dataprivacydata protection regulationssynthetic data for analyticsAI modelingsmart de-identificationtest data managementsoftware developmentdata accuracyprivacy-centric designDocker-ComposeKubernetes

FAQs

What is synthetic data, and how does Syntho generate it?
Synthetic data is artificially created data that mirrors real-world data's statistical properties without containing actual sensitive information. Syntho uses AI and machine learning models to generate this data, ensuring it closely resembles the original while maintaining privacy.
What are the key benefits of using Syntho's synthetic data generation platform?
The platform offers enhanced privacy by design, compliance with data protection regulations, increased speed and flexibility in data-driven projects, and simplified data collaboration across stakeholders.
How does Syntho ensure the quality of its synthetic data?
Syntho uses stringent quality assurance methods, including data quality metrics and external evaluations, to ensure the synthetic data's accuracy and reliability, aiming to create "synthetic data twins".
What types of data does Syntho support, and what are its limitations?
The platform supports structured, tabular data, including categorical, numerical, and geographic data. However, its suitability for specific data types may require assessment based on individual needs.
How secure is Syntho's platform, and how does it protect sensitive data?
Syntho offers AI-powered PII scanning and smart de-identification features, providing deployment options like on-premise and private cloud to ensure sensitive data never leaves the customer's trusted environment.
What are the different deployment options for Syntho's platform?
Syntho Engine can be deployed on-premise, in a private cloud, or other environments and is packaged in a Docker container for seamless integration.
What is Syntho's pricing model, and what factors influence the cost?
The pricing model is based on features rather than usage. Factors influencing cost include the license tier and additional deployment locations, which determine the features, users, and connectors available.
What kind of support and documentation does Syntho provide?
Comprehensive documentation, a ticket support system, direct communication channels, and personalized onboarding training are provided to assist users effectively.
How long does it take to generate synthetic data using Syntho?
The data generation time is contingent on database size, with tables under 1 million records typically taking less than 5 minutes to process.
What are some examples of how Syntho's synthetic data is used in different industries?
It is utilized in healthcare for AI model training, finance for fraud detection, and various other industries requiring large, realistic datasets for testing, analytics, or AI/ML model development.