Our Innovation Analysts recently looked into emerging technologies and up-and-coming startups working on big data solutions. As there is a large number of startups working on a wide variety of solutions, we want to share our insights with you. This time, we are taking a look at 5 promising synthetic data startups.
Heat Map: 5 Top Synthetic Data Startups
Using our StartUs Insights Platform, covering 1.116.000+ startups & emerging companies, we looked at innovation in the field of big data. For this research, we identified 56 relevant solutions and picked 5 to showcase below. These companies were chosen based on a data-driven startup scouting approach, taking into account factors such as location, founding year, and technology among others. Depending on your specific criteria, the top picks might look entirely different.
The Global Startup Heat Map below highlights 5 startups & emerging companies developing synthetic data solutions. Moreover, the Heat Map reveals regions that observe a high startup activity and illustrates the geographic distribution of all 56 companies we analyzed for this specific topic.
DataGen – Human-Focused Data
Many Artificial Intelligence (AI) projects depend on sufficiently large and labeled data sets. Although there are advancements in neural network architectures and training algorithms, these algorithms do not solve the data problem. Machine and deep learning models have achieved high accuracy in various fields of biology and medicine. However, most of the potential remains unrealized due to the lack of open data. Due to their confidentiality, this is seen in situations where medical and clinical records are involved. Healthcare synthetic data generates human-focused data to overcome the lack of open data.
Israeli startup Datagen provides a sophisticated, photorealistic 3D reconstruction of human hands, face, body, and eyes. The technology recognizes gestures and real-world hand-to-object and hand-to-hand interactions. To create human-focused data the simulator blends computer graphics and data generation technology. Additionally, the simulated data incorporates natural 3D skeletal tracking that enables interaction in a Virtual Reality (VR), Augmented Reality (AR), or Internet of Things (IoT) environment.
Cvedia – Photo-Realistic Simulation
The spread of synthetic data technology is justified by its flexibility in an ever-evolving world. AI is easier to supplement and modify to increase the effectiveness of any trained model. Effective training of neural networks for processing video information requires large arrays of images with real annotations. Access to these arrays is a major obstacle for most companies wishing to enter this market. A sufficient number of diverse, high-quality, and labeled images is crucial for the training and validation of today’s visual AI solutions.
US-based startup Cvedia develops a high-fidelity simulator to generate entropic scenes, conditions, and metadata to enable real-time simulations. The startup has proprietary tools to create synthetic images that simplify the sourcing of large volumes of labeled, real, and visual data. Moreover, the simulation platform employs multiple sensors to synthesize photo-realistic environments resulting in empirical dataset creation.
Hazy – Fraud Detection
Synthetic data enables data scientists and developers to train models for projects in areas where big data capability is not available or if it is difficult to access due to its sensitivity. For example, the fintech industry prevents the collection of real user data, as it poses a high risk of fraudulence. Although banks are required to provide APIs for transparency and data safety, synthetic data is better suited for the tasks of detecting fraud and other potential risks.
UK-based startup Hazy generates datasets to boost fraud and money laundering detection to combat financial crime. The synthetic data for forecasting and simulation enables banks and fintech companies to explore large-scale externalities. As threat detection accuracy is high, synthetic data reduce chargeback costs and improves credit risk assessment. Additionally, synthetic datasets enhance the detection of fraudulent insurance claims.
AI.Reverie – Data Labeling
The use of synthetic data for data labeling and benchmarking improves the accuracy of neural networks. Additionally, this process enables an active reduction in distortion and reduces the amount of necessary real data, saving time and money. For small companies, access to labeled datasets is limited, expensive, or unavailable. As a result, synthetic data generation enables companies and researchers to create data labeling solutions for training and even pre-training machine learning models.
US-based startup AI.Reverie offers end-to-end data solutions for data generation, labeling, and benchmarking. Data labeling technology generates synthetic data to train computer vision algorithms for activity recognition, object detection, and segmentation. Furthermore, the benchmarking framework splits the real-world labeled dataset to determine the outcome of the real-world baseline. This results in an overall increase in the performance of the algorithm.
ANYVERSE – Sensor-Specific Data
Real data has many limitations that synthetic data does not have. This is a modeling of complex boundary cases and an accurate synthesis of the client’s entire target system such as lens, sensors, and processing distortions. Furthermore, this leads to the generation of data sets that are GDPR compliant. The ability to reduce the distortion of image bias through input sensor data is an important part of the concept of sensor-specific synthetic data generation, which leads to the efficient use of AI.
Spanish startup ANYVERSE simulates multiple scenarios to create synthetic datasets using raw sensor data, image processing functions and applying custom LiDAR settings for the automotive industry. This technology defines the number of variation cycles, ground-truth data, and channel outputs to create synthetic data solutions for advanced perception models. Custom optical and LiDAR sensors enable the capture of high definition raw data in addition to the usual red, green, and blue.
What About The Other 51 Solutions?
While we believe data is key to creating insights it can be easy to be overwhelmed by it. Our ambition is to create a comprehensive overview and provide actionable innovation intelligence so you can achieve your goals faster. The 5 synthetic data startups showcased above are promising examples out of 56 we analyzed for this article. To identify the most relevant solutions based on your specific criteria, get in touch.