Top 8 Synthetic Data Providing Startups in the US

Synthetic data is quickly reshaping the future of AI, offering a smart solution to one of the biggest challenges in machine learning—access to large, high-quality datasets. With industries like healthcare, finance, and autonomous vehicles facing data privacy issues and the difficulty of gathering real-world data, synthetic data provides a safe and scalable alternative. Valued at $23.3 billion by 2030, the synthetic data market is growing fast. This surge is driven by the rising need for privacy-preserving data and the growing demand for diverse datasets that fuel AI innovation.

Geoffrey Moore, a renowned technology author, stated: “Without big data, you are blind and deaf and in the middle of a freeway.” In just a few years, the number of startups focused on synthetic data has soared, from fewer than 10 in 2017 to over 50 by 2022. As industries look for new ways to overcome data limitations, these startups are playing a critical role in shaping the future of AI.

1. Gretel AI

Founders: Ali Golshan, Alexander Watson, John Myers
Work with synthetic data: Gretel AI specializes in creating advanced synthetic data solutions focused on privacy and security. Their platform enables developers and data scientists to generate high-quality synthetic datasets through easy-to-use APIs, mimicking real-world data while safeguarding against privacy risks and adversarial attacks. With tools to assess data accuracy and utility, Gretel AI allows users to share private data, augment limited datasets, and reduce bias. They also offer a free, no-code tier for synthesizing, transforming, or classifying data, making their technology accessible to a broader audience.
Funding in 2024: $30 million Series B round
Valuation: Around $200 million

2. Synthesis AI

Founders: Yashar Behzadi
Work with synthetic data: Synthesis AI focuses on generating synthetic data for computer vision and perception AI applications. Their work includes creating digital twins for virtual design and camera optimization, simulating edge cases to cover critical and rare events, and mitigating bias by generating diverse, balanced datasets. Synthesis AI also provides synthetic data with detailed 3D world annotations for spatial computing, AR/VR, and robotics. By enabling developers to test and optimize system performance in virtual environments, they help streamline the design process before physical implementation.
Funding in 2024: $20 million Series A round
Valuation: Approximately $100 million

3. ExactData

Founders: John Dawson
Work with synthetic data: ExactData specializes in automating the generation of large sets of fully artificial, engineered test data. Their focus is on creating synthetic data that meets consistency and realism requirements. This suggests that they work on producing high-quality synthetic datasets for testing and development purposes, likely across various industries and applications.
Valuation: $45.8 million (estimated)

4. GenRocket

Founders: Garth Rose
Work with synthetic data: GenRocket specializes in real-time synthetic data generation for software testing and quality assurance. Their platform allows test engineers and developers to create high-quality datasets tailored to specific test cases, ensuring data accuracy, consistency, and completeness. It supports functional and non-functional testing, populates new data environments, and helps train machine learning algorithms. GenRocket also offers dynamic data insertion for simulating real-world scenarios and streamlining the test data lifecycle, eliminating the need for traditional test data management tasks.
Valuation: $3.4 million per year (estimated)

5. Tonic.ai

Founders: Ian Coe, Adam Kamor, Andrew Colombi, Karl Hanson
Work with synthetic data: Tonic.ai generates high-quality synthetic data that mimics real production data while preserving privacy. Their platform allows developers to create secure, scalable datasets for development and testing without exposing sensitive information. Along with data masking and subsetting capabilities, Tonic.ai accelerates development by offering easy-to-use tools that support various data types and environments. The platform also automates and scales synthetic data generation, ensuring teams can hydrate non-production environments with realistic data to improve testing and QA processes.
Funding in 2024: $35 million Series C round
Valuation: Approximately $300 million

6. Bifrost 

Founders: Aravind Kandiah
Work with synthetic data: Bifrost AI specializes in creating AI-generated synthetic data for computer vision applications. They offer a platform that allows users to generate diverse datasets quickly and cost-effectively, including 3D world generation with perfectly labeled datasets. Their synthetic data engine supports various data export formats and is particularly useful for geospatial analytics, defense and intelligence, maritime scenarios, and autonomous systems.
Total Funding: $5.1 million (estimated)

7. Rendered. AI

Founders: Nathan Kundtz
Work with synthetic data: Bifrost AI specializes in creating AI-generated synthetic data for computer vision applications. They offer a platform that allows users to generate diverse datasets quickly and cost-effectively, including 3D world generation with perfectly labeled datasets. Their synthetic data engine supports various data export formats and is particularly useful for geospatial analytics, defense and intelligence, maritime scenarios, and autonomous systems.
Total Funding: $6 million

8. Lexset

Founders: Francis Bitonti and Azam Khan
Work with synthetic data: Lexset focuses on generating synthetic data for computer vision and AI applications using a 3D rendering engine. Their process turns 3D CAD files and scans into thousands of images with varied lighting, occlusions, and angles. Lexset’s Seahaven platform offers unlimited data generation, advanced camera controls, and fully annotated datasets like RGB images and depth maps. Key applications include object recognition, visual search, and real-time 3D tracking. With integration into tools like NVIDIA TAO Toolkit, Lexset enables fast, customizable training for deep learning models in AI-driven computer vision.
Funding: $705K in 2024