Datagen Named a Cool Vendor by Gartner®

Datagen Recognized in the Gartner® 2022 Cool Vendors in AI for Computer Vision Report

Tel-Aviv, Israel and New York, June 9, 2022 – Datagen, the data-as-code leader for computer vision (CV) artificial intelligence, was named a 2022 Cool Vendor in AI for Computer Vision. According to Gartner, “this research is designed to highlight interesting, new and innovative vendors, products and services.”

“We are honored to be named a 2022 Gartner Cool Vendor, as we believe it as validation for our technology and for the growing need for computer vision teams to accelerate AI-powered innovation across sectors by removing the training data barrier,” said Ofir Zuk, co-founder and CEO of Datagen.

Today, there are a myriad of use cases for Computer Vision (CV) and Artificial Intelligence (AI), ranging from automotive to home security, yet one of the biggest barriers to accelerating production-ready models is data. According to the Gartner 2022 Cool Vendors in AI for Computer Vision Report, “while enterprises collect a lot of data, they often struggle with identifying or creating appropriate datasets to enhance their model performance for computer vision use cases.” Data experts and computer vision engineers alike are limited by the amount of time and resources it takes to use real-world data to create a diverse dataset that will ultimately produce top performing, production-ready algorithms. In fact, research from Datagen has found that 99% of computer vision professionals have experienced project cancellations.

Since introducing the Datagen platform, the company has defined a new data-as-code category that will serve as the next frontier in data-centric approaches to computer vision AI model development. Most recently, Datagen announced a $50 million Series B as it continues to lead the charge in the simulated training data space for CV applications, focusing on visual synthetic data for humans in context. By providing a self-service platform, Datagen customers get even more granular access and control when customizing their preferred datasets. Datagen makes it easy for computer vision engineers to use synthetic data to responsibly develop and implement AI solutions to better humanity.

“As computer vision teams look to solve the AI bottleneck problem, they require unfettered access to high-variance, high-performance visual data that uniquely fits their domain and unique use cases,” adds Zuk. “In receiving this recognition from Gartner, we feel confident that we are on the right track in delivering a solution that will help them perform better and deploy faster.”

Meet the Team
The Datagen team will be in attendance at this year’s CVPR event in New Orleans (June 19-24). Attendees can visit the team at Booth #813 throughout the event, and learn from two of Datagen’s foremost CV experts at the following workshops:

Machine Learning with Synthetic Data (SyntML)
7th BMTT Workshop on Benchmarking Multi-Target Tracking: How Far Can Synthetic Data Take Us?

Fortune 500 companies rely on Datagen to develop their future products in the worlds of AR/VR/Metaverse, In-cabin Vehicle Safety, Robotics, IoT Security, and more. To explore the Datagen platform, visit here.

Gartner, “Cool Vendors in AI for Computer Vision,” Shubhangi Vashisth, Arun Chandrasekaran, Svetlana Sicular, Anshul Gupta, 26 May 2022. GARTNER and COOL VENDORS are a registered trademark and service mark of Gartner, Inc. and/or its affiliates in the U.S. and internationally and are used herein with permission. Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner’s research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.

About Datagen
Datagen is the Data-as-Code company, a category-defining solution for the Computer Vision data market. Building on the principles of Infrastructure-as-Code, Datagen’s approach turns the heavy operational process of visual data collection and annotation into an easy-to-control programmable user interface, enabling CV teams to generate data, train and evaluate models across the development lifecycle. Fortune 500 companies rely on Datagen’s self-service human-centric synthetic data platform to develop their future products in the worlds of AR/VR/Metaverse, In-Cabin Vehicle Safety, IoT Security and more. Founded in 2018, Datagen is led and backed by world-renowned AI experts. For more information, visit

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Claire Cashdan
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Data-as-Code Co. Datagen Secures $50 Million in Series B Funding Led by Scale Venture Partners

New investment to meet growing demand for its self-service synthetic data platform used by global tech giants’ most advanced computer vision teams

Tel-Aviv, Israel and New York, New York – March 23, 2022 – Datagen, the data-as-code leader for computer vision artificial intelligence (AI), today announced it has closed $50 million USD in Series B financing led by new investor Scale Venture Partners, with participation from existing investors TLV Partners, Viola Ventures and Spider Capital. Andy Vitus, partner at Scale VP, joins Datagen’s board of directors, effective immediately.

This latest round of funding brings Datagen’s total financing to over $70 million USD. The additional funds will help Datagen to continue to bolster its leadership position in the nascent computer vision (CV) space. As one of the fastest growing fields within AI, computer vision is becoming a fully-fledged, market-tested industry in need of a proper infrastructure stack to help supercharge the development of AI and its most imminent applications.

“As we enter a new, data-centric age of machine learning, a streamlined, operationalized data pipeline is poised to be the most lucrative piece of the machine learning puzzle,” said Andy Vitus, Partner at Scale Venture Partners. “This is why we are placing our bets on Datagen, which is creating a complete CV stack that will propel advancements in AI by simulating real world environments to rapidly train machine learning models at a fraction of the cost — this will fundamentally transform the way computer vision applications are developed and tested. The potential impact of what Datagen has to offer, across a broad range of applications, is staggering.”

DGU Is the New Data Compute Unit
A key element of Datagen’s success is its unique focus on CV teams. By providing a self-service platform for CV teams, Datagen makes it easy for those responsible for developing and testing AI products to obtain and use synthetic data. Datagen’s unique offering makes it easy for CV engineers to engage and adopt synthetic data by running Data Generation Units (DGUs) by the hour to produce the data their AI needs. For the first time, buying synthetic data is as easy as buying cloud computing resources.

The proof of Datagen’s success is confirmed by its spectacular 8X growth in total revenue, driven in part by lighthouse accounts that include three of the top five global tech giants, who are using Datagen to bring their AI products and solutions to market.

“The need for robust, high-variance and high-performance training data will continue to grow exponentially as computer vision algorithms and their applications become more numerous and diverse,” said Datagen Co-founder and CEO Ofir Zuk (Chakon). “Our mission is to enable every CV team with the best synthetic data solution to power the development of their AI applications. That’s why we are honored to welcome Scale VP to the community of our marquee investors. With today’s new funding, we are poised to accelerate growth and take the market by storm.”

Building the Infrastructure for Computer Vision
According to a recent industry survey commissioned by Datagen, a stunning 99% of computer vision teams reported having had a machine learning project completely canceled due to insufficient training data. So it’s no surprise that synthetic data is gaining such widespread adoption, with 96% of computer vision teams reporting using synthetic data in some proportion to train computer vision models. Gartner recently placed synthetic data at the top of its list of strategic predictions for 2022 and beyond, saying that “by 2024, 60% of the data used for the development of AI and analytics projects will be synthetically generated.”

Datagen was founded in 2018 by computer vision experts Ofir Zuk (Chakon) and Gil Elbaz with a mission to reinvent how AI/ML teams gather data needed for computer vision network training. The company’s unique simulated data (a form of synthetic data) technology obviates the need for producing or sourcing scant 2D and 3D training data for computer vision AI development.

Today, Datagen is defining a new data-as-code category that will serve as the next big frontier following the model- and data-centric approaches to computer vision AI development. By removing the need to source and manually annotate training data, some of its most time-consuming and resource-intensive steps, Datagen is helping CV teams get to market faster with applications in augmented reality, smart offices, automotive in-cabin monitoring, home security, and more.

New Industry Forum
As part of today’s announcement, Datagen is launching a Visual Synthetic Data Forum, a new industry forum that will convene quarterly and focus on topics related to data for computer vision AI development. Under Datagen’s leadership, it will aim to create a strong networking group focused on the advancement of synthetic data development by bringing esteemed leaders from academia and the industry to help shape and accelerate future AI applications and use cases built on computer generated synthetic data.

About Scale Venture Partners
Scale is an early-stage venture capital firm focused on intelligent business software, with a Scaling Platform that has helped companies like, DocuSign, HubSpot, JFrog, Root, and WalkMe move from founder-led growth to a repeatable go-to-market machine. Today we’re focused on the next generation of companies building Cognitive Applications, like:, Forter, Locus Robotics,, Socure, Techsee, and Learn more at

About Datagen
Datagen is leading the AI revolution by generating synthetic data to train computer vision systems, with expertise in creating data for human-centric computer vision applications. We developed a self-serve synthetic data generation technology that delivers visual data with unmatched domain coverage and high-variance. Using our platform, CV teams generate high-fidelity 3D data with associated ground truth, in a seamless and scalable way. Datagen customers include Fortune 100 companies across a variety of industries including AR/VR, Security, Automotive, Robotics and more. Founded in 2018, Datagen is led by recognized AI experts and is backed by AI industry luminaries. For more information, visit

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Anya Nelson
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New Study Finds Over 96% CV Teams Already Using Synthetic Data for Training and Testing

A survey by Datagen reveals widespread adoption of synthetic data throughout the CV field to advance AI/ML applications.

TEL AVIV, Israel — December 21, 2021 — Datagen, the leader in synthetic data generation on a mission to bring data simulation to every computer vision engineer, today announced the release of a new research study, “Synthetic Data: Key to Production-Ready AI in 2022,” exploring training data in the field of Computer Vision (CV). The study reveals a once fragmented field beginning to coalesce around the promise of synthetic data to help mitigate frequent project delays and cancellations. 

The study emphasizes that training data has become a significant stumbling block for computer vision professionals, who cited a number of data-related complications hindering their organization’s progress in CV. Among the data-related issues experienced, the most prevalent were: 

  • Wasted time and/or resources caused by a need to retrain the system often (52%)
  • Poor annotation resulting in quality issues (48%)
  • Poor data coverage of the intended application’s domain (47%)
  • Lack of sufficient amount of data (44%)

All four of these problems can seriously jeopardize a project’s progress, making their widespread presence of significant concern to CV teams. As a result of these issues, the overwhelming majority of computer vision teams struggle with frequent, lengthy project delays, and even outright cancellations. Inadequate training data has led to an environment in which: 

  • 99% of respondents have experienced project cancellations
  • 80% have experienced project delays lasting at least 3 months
  • 33% have experienced project delays lasting 7 months or more                 

The frequency, length, and ubiquity of data-driven project disruptions in the field of computer vision are immense. However, the study also revealed several trends that indicate a growing appetite for synthetic data. The research revealed that a staggering 96% of computer vision teams reported already using synthetic data in the training and testing of their computer vision models

Based on the survey findings, this surge in synthetic data adoption can be attributed to the fact that its many benefits are both broadly understood and broadly experienced by the computer vision community. For example, when asked what the primary motivation was behind their organization’s use of synthetic data, CV teams reported testing, training, and addressing edge-cases in near equal measure. Similarly, when asked about their first-hand experience, respondents reported experiencing the following benefits of synthetic data:

  • Reduced time-to-production (40%)
  • Elimination of privacy concerns (46%)
  • Reduced bias (46%)
  • Fewer annotation and labeling errors (53%)
  • Improvements in predictive modeling (56%)  

“Synthetic data is the future of data. This is the new way to control and consume the data our AI systems need,” said Ofir Chakon, founder and CEO of Datagen. “As simulation gets better over time, with all its benefits, it will take over the place of labor-intensive manual data collection that is no longer scalable at the speed the world is evolving.”

The survey, which was commissioned by Datagen and conducted by Wakefield Research, polled 300 computer vision professionals, from 300 unique organizations across a variety of industries. The survey set out to better understand how computer vision teams obtain and use AI/ML training data for computer vision systems and applications, and how these choices impact their work. The accompanying report also features commentary and insights from leading industry experts and innovators. To access the full report:

About Datagen

Datagen is leading the AI revolution by generating synthetic data to train computer vision systems, with expertise in creating data for human-centric computer vision applications. We developed a self-serve synthetic data generation technology that delivers visual data with unmatched domain coverage and high-variance. Using our platform, CV teams generate high-fidelity 3D data with associated ground truth, in a seamless and scalable way. Datagen customers include Fortune 100 companies across a variety of industries including AR/VR, Security, Automotive, Robotics and more. Founded in 2018, Datagen is led by recognized AI experts and is backed by AI industry luminaries. For more information, visit

Media Contact:

Kelsey Bates

Scratch Marketing + Media for Datagen

The Metaverse and AI Edge Cases Will Drive Synthetic Data Boom: Top Predictions for 2022 by Synthetic Data Innovator Datagen

Synthetic data is in for a banner year, as businesses look to leverage AI for a growing number of increasingly-sophisticated applications, including tackling the world’s supply-chain disruptions, reinventing automotive safety, and creating a whole new class of intelligent consumer goods with the metaverse at the fore. 

Tel Aviv, Israel — November 30, 2021 — Datagen, the pioneer of domain-specific synthetic data for humans and object perception, today released its new year’s predictions for the fields of Artificial Intelligence, Machine Learning, and Computer Vision. As AI makes its way into ubiquitous adoption by a growing number of industries and applications, the demand for robust training data will expand accordingly. However, with manual data collection already at the limits of its own utility, the race for AI supremacy will only serve to widen the existing gulf between supply and demand. At the same time, companies like Datagen are making it easier and more affordable to generate high-quality synthetic datasets to train computer vision (CV) AI models. The ability to generate tens of thousands of synthetic images — customized to suit the unique parameters of each distinct application — makes synthetic data the obvious solution to the limitations of traditional, manually-collected data.

“We’re approaching a major inflection point for the synthetic data field,” said Ofir Chakon, co-founder and CEO of Datagen. “This year, AI underwent a major paradigm shift, in which traditional, model-centric approaches to AI development were reconsidered in favor of data-centrism, which means data scientists are now placing more significance on the quality of their training data as a determinant of performance, rather than the quality of their model. This shift in the zeitgeist — combined with the ability to rapidly iterate one’s dataset in a targeted, fine-tuned way — will make 2022 the year in which synthetic data becomes the most widely used training and testing solution in AI.”

After a year of building great momentum to power the next big leap in computer vision systems, including key appointments to its executive leadership and advisory board, Datagen’s executive team have predicted the following trends to take center stage in 2022 to help organizations accelerate their AI adoption and to prepare for what comes next: 

The Synthetic Data Revolution Will Create a New ‘Synthetic Data Engineer’ Vocation to Become of the Most In-Demand Jobs

In 2022, a new position will surface — the ‘synthetic data engineer’ — data scientists who handle the creation, processing, and analysis of large synthetic datasets in an effort to support the automation of prescriptive decision-making through visuals. This new vocation, a natural evolution of the computer vision engineer, is already emerging in larger companies, where synthetic data teams have sprouted. The synthetic data engineer will become one of the most sought-after professionals in the AI market as more enterprises and startups alike will need the skills to support their simulated data initiatives. Expect to see such job postings soar and more training courses to become available, to fill the 22% rise in computer and information research scientist jobs over the next 10 years (US Bureau of Labor statistics), of which CV (and synthetic data) engineers are a subset. In addition, we will see other data-related professionals reposition themselves as synthetic data engineers to take advantage of expanding opportunities.

Data-Centric AI Development Will Fuel Widespread Adoption of Synthetic Data

After nearly a decade of being dominated by model-centric approaches to development, the field of AI is experiencing a paradigm shift — away from modeling and toward a data-centric approach to AI development. In short, rather than focusing on making incremental improvements to one’s AI algorithm or model, researchers have found that they can optimize AI performance much more effectively by improving the quality of one’s training data. Over the course of 2021, data-centrism has been rapidly gaining acceptance throughout AI’s R&D and enterprise communities. This trend will undoubtedly continue well into 2022, and the increased focus on data quality will act as yet another catalyst for the adoption of synthetic data.

Technology Needed to Make the Metaverse a Reality Will Experience a Major Expansion

Facebook’s recent announcement about its foray into the metaverse is driving the metaverse mania. Recent metaverse developments include Microsoft’s announcement of its own metaverse, plus a key metaverse patent filing from Apple. Meanwhile, another early metaverse entrant, NVIDIA, saw a 12% increase in stock price since the Facebook announcement.

These recent metaverse announcements are merely the opening salvos in what will surely be a heated competition to define the future of human interaction with the environment and how we manage social connections with remote people. In the frenzy to develop the first practical, real-world applications, vendors will need to invest heavily in tools and technologies that can help them get to market first and gain first-mover advantage. These include a variety of hardware, software and data solutions. Look for a bump in these investments over the next 12-18 months.

Edge Cases Will Continue to Boost Industry Demand for Synthetic Data

Edge cases are unlikely or improbable situations that a given AI may still conceivably encounter over the course of its operational lifetime. Although improbable, engineers need to take these edge cases into consideration when developing and training their AI applications — especially when applications carry significant risks, such as autonomous vehicles. However, the very same risks that make edge case training so important in these applications, also make it exceedingly difficult, if not impossible, to gather the data said training requires. Faced with this conundrum, more and more businesses will turn to synthetic data for their training needs. More and more car manufacturers will use synthetic data to train and develop their in-cabin driver monitoring system (DMS). These AI-enabled systems use computer vision to monitor drivers and issue alerts whenever drivers show signs of distraction or fatigue. We will surely see many other carmakers follow suit over the coming years, as new EU regulations mandating DMS technologies go into effect; and American manufacturers inevitably do the same to keep up with competition. This, along with work on driverless technologies, will vastly expand and deepen the industry’s investment in the human-centered synthetic data needed to train those systems. 

The Supply Chain Crisis Will Worsen but Digital Twins Will Save the Day 

Federal Reserve chair Jerome Powell and other experts predict that the global supply chain crisis will only get worse in 2022 before it gets better. In fact, a recent Wall Street Journal poll of leading economists finds almost half of the respondents cite supply chain bottlenecks as the biggest threat to growth in the next 12 to 18 months. Unpredictable weather patterns and labor shortages will intensify the disruptions caused by the global pandemic. As a result, private businesses and government agencies will turn to solutions that could help alleviate the pressures. One such solution will be digital twins, a machine learning driven simulation of real-world objects to predict disruptions and provide recommendations on how to avoid them. Organizations whose operations are heavily supply chain dependent should consider investing in digital twins technology to stay competitive. 

Across all these predictions, the common thread is clear — the world’s good data needs are going up. And manual data collection and annotation won’t be able to satisfy the impending explosion of demand. Synthetic data, on the other hand, offers a fast, customizable, and cost-effective alternative that, in many cases, performs even better than its real-world counterpart. The world’s increasing demand for data also coincides with an increased demand for data professionals, both data scientists and computer vision engineers, which may well prove to be the true bottleneck to impede AI’s rise to universal adoption.  

About Datagen

Datagen is powering the AI revolution by providing high-performance, synthetic data, with a  focus on data for human-centric computer vision applications. We developed the first self-serve synthetic data platform that generates visual data which is both photorealistic and high-variance. Our platform allows CV Engineers to create high-fidelity synthetic data in a seamless and scalable manner. Fortune 500 companies rely on Datagen to enable their technological innovation in the worlds of AR/ VR/ Metaverse, In-cabin Vehicle Safety, Robotics, IoT Security and more. Founded in 2018, Datagen is led and backed by world renowned AI experts. 

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Kelsey Bates

Scratch Marketing + Media for Datagen


Synthetic Data Pioneer Datagen Builds Momentum to Power the Next Big Leap in Computer Vision Systems

With key appointments to its executive leadership and advisory board, Datagen’s growth in Tel Aviv firms up Israel’s position as the next major hub building data for computer vision and artificial intelligence innovation

TEL AVIV, Israel — September 22, 2021 — Datagen, the pioneer of domain-specific synthetic data for humans and objects perception, today announced strong momentum on the heels of its March 2021 public launch, when the company raised over $18 million from TLV Partners and Viola Ventures. Datagen continues to make forays into the enterprise market, now working with a number of Fortune 100 companies in augmented/virtual reality, robotics, and automotive, including the majority of the top U.S. tech giants. With 60% of the data used for the de­vel­op­ment of AI and an­a­lyt­ics projects expected to be syn­thet­i­cally gen­er­ated by 2024 according to Gartner, Datagen is at the forefront of the artificial intelligence 2.0 revolution. The company’s innovation and rapid growth have attracted Israel’s top computer vision, artificial intelligence and cloud innovators to bolster its executive leadership team and round out its advisory board.

Joining Datagen as Head of AI Research, former Google AI leader Dr. Jonathan Laserson will spearhead the company’s research into AI generating 3D simulations, synthesizing photorealistic images and videos for AI performance acceleration. Tal Darom is joining the Datagen executive ranks as its VP of R&D, following his leading a human-centric computer vision research group at Amazon. During his tenure at Amazon, Darom managed the development of machine learning training and model evaluation infrastructure and was responsible for bringing deep learning algorithms to production. Hailing from Google, Hadas Sheinfeld joins Datagen as its VP of Product. At Google, she was responsible for the infrastructure of massive scale, serving over 100 products and their respective engineering teams. Karine Regev comes on board to bring the company’s innovation to existing and new markets in her role as VP of Marketing. Karine brings a track record of growing brand and market share for market leaders including Alcide, acquired by Rapid7, and Aqua Security

“I am fascinated by the recent developments in the space between graphics and deep learning,” said Dr. Jonathan Laserson, Head of AI Research at Datagen. “The new methods use a radically different approach compared to classic graphics tools, and achieve new highs of photorealism. Datagen is in the business of making synthetic images capture as much of the world as possible, and look as real as possible, so naturally Datagen is well positioned to be at the forefront of this new revolution and I’d like to be part of it.”

The new executive leadership will work closely with Datagen’s advisory board, which includes well-respected members of the AI community. Collaboration with academic institutions will ensure Datagen has access to cutting-edge research, thanks to its visionary investors, including Brown University Professor Michael J. Black, founding director of the Max Planck Institute for Intelligent Systems, and UC Berkeley Professor Trevor Darrell, founding co-director of the Berkeley Artificial Intelligence Research (BAIR). Datagen is looking at the market comprehensively from both hardware and software applications, bringing in advisors Lihi Zelnik, head of Alibaba DAMO Academy Machine Intelligence Israel Lab, and Gal Chechik, director of AI at Nvidia. Finally, Anthony Goldbloom, co-founder and CEO of Kaggle, an online community of data scientists and machine learning practitioners (subsidiary of Google), offers continued access to a pool of industry talent.  

Datagen builds on Israel’s rich legacy of bringing computer vision innovation to the market. According to Crunchbase, there are more than 680 artificial intelligence companies in Israel that have collectively raised $4.5B. This growth explosion over the last few years is due largely in part to the high concentration of engineers and Israel’s world-renowned universities. These academic institutions provide access to talent and cutting-edge new technology development in the space. In the last two months, Datagen has hired more than 20 employees and plans to bring on additional team members across sales and marketing, software and DevOps, and product departments. 

“As Datagen shapes this emerging field of synthetic data, I could not be more proud to have such esteemed minds join our leadership team and advisory board,” said Ofir Chakon, co-founder and CEO of Datagen. “Israel is a hotbed for artificial intelligence and computer vision innovation but requires diverse and comprehensive datasets to train, test and validate machine learning applications. These powerhouse executives and advisors will help accelerate Datagen’s market opportunities as synthetic data increasingly becomes the core layer in computer vision systems.”

About Datagen

Datagen is leading the AI revolution by generating photo-realistic data to train computer vision systems, with expertise in human-object Interaction. We developed a data-centric technology that delivers visual data with unmatched domain coverage and fully-controlled object variance. Using our platform, companies generate high-fidelity 3D data with associated ground truth, in a seamless and scalable way. Datagen customers include Fortune 100 companies across a variety of industries. Founded in 2018, Datagen is led by recognized AI experts and is backed by AI industry luminaries. For more information, visit 

Media Contact:

Kelsey Bates

Scratch Marketing + Media for Datagen