AuraML Raises $230,000 In Pre Seed Round Led By IAN
AuraML, a synthetic image data platform for computer vision and robotics, raises $230,000 from IAN
The fund raise will help AuraML to augment its core engineering team and expand its geographic footprint
AuraML, a synthetic image data platform for computer vision and robotics, has raised $230,000 from IAN in pre-seed funding from Indian Angel Network (IAN). Uday Sodhi, . Neeraj Saran and KRS Jamwal, the lead angel investors from IAN led the round. The company intends to utilize the investment to optimize its synthetic data generation engine, hire its founding engineering team and expand its footprints in the US and European markets. AuraML also plans to use the capital for the official launch of its cloud platform in the coming months. Additionally, the startup is focusing its efforts on building cutting-edge generative AI-based IP.
Launched in January 2023, AuraML is the brainchild of Ayush Sharma and Arjun Gupta, both with significant experience in robotics and AI/ML. They encountered significant challenges in data collection and labeling while training machine learning models for computer vision in their previous avatar. This coupled with the rise in concerns about data sharing and strict privacy laws made the problem even more acute and therefore the need for a critical solution.
The recent advancements in 3D graphics and generative AI have made creating synthetic images and scenarios that are hyper realistic possible. This inspired Ayush and Arjun to develop a cloud-based synthetic data platform that would enable computer vision engineers to easily create synthetic image datasets in a cost-effective and time-saving manner without having to rely on manual data collection and labeling.
Ayush Sharma, CEO, AuraML, said, “We are grateful to IAN for trusting us and supporting us in our growth journey. This investment will be a key accelerator for us to take the technology from a prototype to a fully functioning product. We plan to utilize these funds to hire our founding engineering team, develop core technologies and expand our global presence in the US and European markets. This funding will also help us work on building cutting-edge generative AI-based IP.”
Padmaja Ruparel, IAN, said, “Even in today’s digitally-driven times, the data collection and labeling process is completely manual. Millions of images need to be labeled by humans for training computer vision algorithms. This costs companies a lot of time and money. Additionally, the challenges related to data privacy and sharing and data collection of rare cases cannot be ruled out. AuraML, with its synthetic image data platform, is on a mission to offset all these concerns and improve the accuracy of the ML models, thereby allowing complete control over the generated dataset. As they have embarked on a new journey, we are happy to help them grow and support them in all ways possible.”
Headquartered in Bengaluru, AuraML has developed a proprietary synthetic data engine and is currently running closed projects with its initial customers. It has received positive feedback as customers have seen improvements in the accuracy of models trained using the startup’s synthetic data. Simultaneously, the company is working on developing an innovative technology using generative AI that would enable people to create realistic-looking 3D worlds and images. AuraML’s key focus areas for the next year are developing synthetic datasets for computer vision particularly targeting autonomous robots, warehouse and logistics automation and Industry 4.0 use cases.
Synthetic data market share was valued at $163.8 million in 2022 and is projected to reach $3.5 billion by 2031, growing at a 35.8% CAGR. It is expected that synthetic data will completely replace real data as the main source of training ML models in the future. AuraML sees this as an opportunity to build a brand in the market and drive the next revolutionary wave of advanced AI. The startup envisions building a world powered by computer vision systems trained on its synthetic data that would make people’s lives better and easier.