Top Synthetic Data Tools/Startups For Machine Learning Models in 2023

Information created intentionally rather than as a result of actual events is known as synthetic data. Synthetic data is generated algorithmically and used to train machine learning models, validate mathematical models, and act as a stand-in for test production or operational data test datasets.

The advantages of using synthetic data include easing restrictions when using private or controlled data, adjusting the data requirements to specific circumstances that cannot be met with accurate data, and producing datasets for DevOps teams to use for software testing and quality assurance.

Constraints when attempting to duplicate the complexity of the original dataset might lead to discrepancies. It is impossible to completely substitute accurate data because precise, accurate data are still needed to generate practical synthetic examples of the information.

How Important Is Synthetic Data?

To train neural networks, developers require vast, meticulously annotated datasets. AI models are typically more accurate when they have more varied training data.

The issue is that compiling and identifying datasets that could include a few thousand to tens of millions of items takes a lot of effort and is frequently unaffordable.

Now comes the fake data. Paul Walborsky co-founded one of the first specialized synthetic data services, AI.Reverie thinks that a single image that may cost $6 from a labeling service can be synthetically generated for six cents.

Saving money is just the beginning. By ensuring you have the data diversity to accurately reflect the real world, synthetic data is essential for dealing with privacy concerns and decreasing prejudice, continued Walborsky.

Synthetic datasets are sometimes superior to real-world data since they are automatically tagged and can purposefully include uncommon but critical corner situations.

List of synthetic data startups and companies


Israeli firm Datagen was founded in 2018 and has funded $22 million, including an $18.5 million Series A in February that served as the business’s formal coming-out celebration. As it primarily concentrates on photorealistic visual simulations and recreations of the natural world, with apparent expertise in human motion, Datagen refers to its particular flavor of synthetic data as “simulated data.” Datagen uses generative adversarial networks, an AI method that is becoming more and more common, like many other businesses that deal with synthetic data (GANs). It resembles a game of computer chess between two systems, but one generates fictitious data while the other assesses the veracity of the outcome. In a Physical Simulator, the business combines GANs with something called Reinforcement Learning Humanoid Motion Techniques and super-rendering algorithms to produce

Datagen targets several industries, including retail, robotics, augmented and virtual reality, the Internet of Things, and self-driving automobiles. Consider retail automation in the shape of an Amazon Go location, where a computer vision system monitors shoppers to ensure no one leaves with any five-finger discounts.

Parallel Domain

Simulating surroundings for self-driving vehicles is perhaps one of the most prevalent use cases today. That is the main line of business for Parallel Domain, a Silicon Valley startup that was established in 2017 and which we previously profiled. Since then, the company has raised around $13.9 million, including an $11 million Series A at the end of the previous year. Toyota is likely its most significant backer and client (TM). To educate self-driving cars on how to avoid killing people, the business concentrates on some of the most challenging use cases for its synthetic data platform. Its most recent development, made in partnership with the Toyota Research Institute, teaches autonomous systems about object permanence using synthetic data. Though AI can now track objects even when they temporarily vanish partly because of Parallel Domain, current perception systems are still like infants playing peek-a-boo. Additionally, the business has made its data visualizer for fully annotated synthetic cameras and LiDAR datasets available to the public. The company offers artificial training data for autonomous drone deliveries and autonomous driving.


An estimated $6.5 million has been raised by the UK business Mindtech, which was founded in 2017. A $3.25 million Seed round was completed just last month. One famous investor is In-Q-Tel, a US government organization that finances innovations with the potential to help organizations like the CIA one day. So, there you go. The modular tool Chameleon, developed by Mindtech, allows users to instantly create an infinite number of settings and scenarios using photorealistic 3D models. According to the business, Chameleon is specially made to assist its clients in developing AI systems that “understand and predict human interactions.” Along with providing services to espionage agencies, Mindtech also offers products and services to the retail, smart home, healthcare, transportation, and robotics industries.

Synthesis AI

2019 startup Synthesis AI raised $4.5 million in a Seed round with iRobot (IRBT) in April, likely to further its robotic vacuums for intelligent homes. Like Datagen, Synthesis uses GANs with computer-generated image (CGI) technology, employed in nearly every modern film, to construct synthetic humans. FaceAPI, the company’s debut offering, allows companies to create more powerful AI facial models for intelligent assistants, teleconferencing, driver monitoring, and smartphone facial verification. To enhance AI models’ ability to represent a variety of facial kinds, Synthesis AI released 40,000 original high-resolution 3D facial models in June.


OneView is an Israeli startup founded in 2019 and raised $3.5 million. The business’s primary goal is to supply artificial data to AI algorithms that generate geographic intelligence from satellite and aerial photos. Large portions of the planet, including cities, airports, harbors, and other structures, are frequently seen in these views. OneView uses actual data from the open-source data mapping service OpenStreetMap to create the foundation model for the synthetic dataset. The firm simply converts a 2D image into a 3D one rendered numerous times to replicate diverse situations, including objects, weather, lighting, etc. You can read more about the process here.


Enterprises can access, share, correct, and simulate data thanks to MOSTLY AI’s market-leading, most accurate Synthetic Data Platform. Because of advancements in AI, synthetic data from MOSTLY AI has the same appearance and feel as actual data, can maintain important granular-level information, and always ensures that no one is ever exposed.


By enhancing the caliber of training datasets, YData offers a data-centric platform that speeds up the creation and raises the return on investment of AI solutions. Data scientists can now enhance datasets using cutting-edge synthetic data generation and automated data quality profiling.


Hazy sets itself apart from the competition by providing models that can offer high-quality synthetic data with a differential privacy mechanism. In a relational database, data might be tabular, sequential (including time-dependent events, like bank transactions), or spread throughout multiple tables.


A provider of AI solutions, CVEDIA creates “synthetic algorithms”—off-the-shelf computer vision algorithms utilizing fake data. More than 10 hardware, cloud, and network deployment options are available for CVEDIA algorithms. SynCity, CVEDIA technology was created using data science and deep learning theory based on their own simulation engine. The organization works across manufacturing, aerospace, smart cities, utilities, infrastructure, and security industries.


Full Stack Machine Learning and Computer Vision with Data Generation Platform for Data Scientists allowing AI Business Transformation at scale.

Building ideal, customized AI models from the start and training them in virtual reality are both made possible by the SKY ENGINE AI Platform. Before deployment in the real world, your sensor, drone, or robot can be trained and tested in a virtual environment using the SKY ENGINE AI software.

By providing perfectly balanced datasets for Computer Vision applications like object detection and recognition, 3D positioning, pose estimation, and other complex cases like analysis of multi-sensor data such as Radars, Lidars, Satellite, X-rays, and more, SKY ENGINE AI Synthetic Data Generation makes the lives of Data Scientists easier. is a data factory that works with startups and Fortune 500 companies to generate AI training photos and videos and annotate data. To train the most sophisticated AI vision and video recognition algorithms and AI agents in the sectors of security, retail, healthcare, agriculture, industry 4.0, and similar, at-scale data labeling is a critical need that helps to address.


Modern data privacy technology created by Statice enables businesses to increase data-driven innovation while preserving individual privacy. Companies can produce privacy-preserving synthetic data that is compatible with any sort of data integration, processing, and dissemination thanks to the privacy assurances of the Statice data anonymization program. With Statice, enterprises in the financial, insurance, and healthcare sectors can boost data agility and enable value generation across their data lifecycle. Utilize Statice to securely train machine learning models, process your data in the cloud, and share it with partners.


A Spanish firm called ANYVERSE uses LiDAR, image processing, and raw sensor data to produce synthetic datasets for the car sector. The startup’s solution specifies how many variation cycles, real-world data, and output channels should be used to create synthetic data. This enables deep learning training for sophisticated perception models to be simpler for automobile original equipment manufacturers (OEMs) and suppliers.

Synthetic data modeling provides an exact synthesis of the client’s whole target system using sophisticated boundary cases. Additionally, this produces data sets that are GDPR compliant and have slight image bias. This enables businesses to reduce costly data collecting procedures and quick model training. Some startups provide platforms that let customers specify the target system they want to utilize to generate data, making use-case-specific data more accurate and easily accessible.

Compared to using or acquiring real-world data, is the Platform as a Service (PaaS) for data scientists, data engineers, and developers who need to create and deploy unlimited, customized synthetic data generation for machine learning and artificial intelligence workflows. This reduces costs, closes gaps, and eliminates bias, security, and privacy concerns.

By providing a collaborative environment, samples, and cloud resources to get started right away defining new data generation channels, creating datasets in high-performance computing environments, and providing tools to characterize and catalog existing and synthetic datasets, moves the process of creating and utilizing synthetic data closer to the business need.


Data scientists may significantly raise the performance of their machine-learning models with Datomize. Since the lack of high-quality data and the resource-intensive process of feature engineering are the main obstacles to creating high-performing ML models, Datomize provides data scientists with an unlimited supply of data of exceptional quality and variety while automatically creating a comprehensive set of cutting-edge features. The Datomize platform enhances the original data with exceptionally high-quality synthetic data, automatically develops features that improve the performance of ML models, fills in any gaps in the data, balances the data with adequate representation of every class to prevent biased models, and enables the simulation of novel scenarios using rules-based data generation.


Facteus is a source of valuable financial data insights. Facteus safely transforms raw financial transaction data from legacy technologies into actionable information that can be used for machine learning, artificial intelligence, data monetization, and other strategic use cases without compromising data privacy through its ground-breaking, patent-pending synthetic data process. Business and investment executives now have access to the “truth” of actual consumer financial transactions, not just broad patterns, thanks to the company’s data products, which have been collected directly from over 1,000 financial institutions, payment providers, fintech, and debit card programs.


Gretel provides developers, data scientists, and AI/ML researchers with safe, quick, and simple access to data without sacrificing accuracy or privacy, thus resolving the issue of the data bottleneck. Gretel’s APIs were created by developers for developers, making it simple to create anonymous and secure synthetic data so you can protect your privacy and innovate more quickly.


Synthesized aims to make it quick and straightforward to create and retrieve high-quality data. Thanks to an API, the company invented the first platform that generates better data than production data in minutes. Data is automated using straightforward YAML configurations and integrates quickly into CI/CD workflows, so software or data engineers are not required. Without manual setups, QA and ML teams can now quickly create, validate, and securely share high-quality data for software testing, model training, and data analysis.


Due to the significant tension between data privacy and data utility, public and private enterprises are exposed to substantial dangers while handling sensitive data. To ensure that organizations utilize their maximum data potential while being fully compliant, Syntheticus offers a solution that leverages cutting-edge Deep Learning to generate synthetic data for various file formats.

Artificial data, data privacy, deep learning, GDPR, software as a service, machine learning, artificial intelligence (AI), cloud computing, privacy technology, HIPAA, data analytics, and privacy shield


With its headquarters in Amsterdam, Netherlands, Syntho is a data technology company with a strong background in privacy-enhancing technologies (PET). It was formed in 2020 to overcome the privacy conundrum and enable the open data economy, where data may be utilized and shared freely and privacy assured. To access your data and allay valid privacy worries, Syntho offers privacy-preserving synthetic data.


Tonic enables businesses to produce secure, synthetic replicas of their data for use in software development and testing, empowering developers while safeguarding consumer privacy. The company, founded in 2018 and has headquarters in Atlanta and San Francisco, is a leader in enterprise technologies for database subsetting, de-identification, and synthesis. Tonic data is used daily by thousands of developers in fields as diverse as healthcare, financial services, logistics, edtech, and e-commerce to build solutions more quickly. Tonic develops cutting-edge solutions while collaborating with clients like eBay, Flexport, and PwC to further their mission of promoting individual privacy rights while empowering businesses to perform at their highest levels.

Clearbox AI

Clearbox AI offers a product called Enterprise Solution, based on proprietary technology and powered by a unique combination of generative AI models which produce high-quality structured synthetic data.

Note: We tried our best to make this list, but if we missed anything, then please feel free to reach out at

Prathamesh Ingle is a Mechanical Engineer and works as a Data Analyst. He is also an AI practitioner and certified Data Scientist with an interest in applications of AI. He is enthusiastic about exploring new technologies and advancements with their real-life applications

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