As companies across various fields, from entertainment to agriculture, retail to robotics, rush to implement AI to their business practices, they repeatedly face obstacles including efficient data labeling at scale.
As per the report, the AI market is projected to become a $190B (approx €162B) industry by 2025, but around 96% of companies face problems with data labeling when it comes to AI implementation and production. Sorting out huge unstructured data through labeling and management is the first step towards the development of AI models.
Based out of Tel-Aviv, Dataloop’s proprietary SaaS platform brings human and machine intelligence together, not only for training and labeling data but also for powering enterprises successfully in production.
The Israeli SaaS platform has raised $16M (approx €14M) in funding after the completion of the $11M (approx €9.3M) Series A round. The funding round was led by Amiti Ventures with participation from F2 Venture Capital, OurCrowd, NextLeap Ventures, and SeedIL Ventures.
The company intends to use the company to increase recruitment efforts and grow its presence in the US and European markets.
Eliminates data challenges
Founded in 2017, Dataloop eliminates the data challenges companies face allowing them to focus resources on their core business. The company’s platform consistently feeds ‘real time’ data back to human counterparts while simultaneously streamlining the workflow with automated annotation tools.
“Many organisations continue to struggle with moving their AI and ML projects into production as a result of data labeling limitations and a lack of real-time validation that can only be achieved with human input into the system,” says Eran Shlomo, CEO of Dataloop.
By keeping humans in the loop, algorithms can create more accurate and reliable predictions in less time, at scale, and on budget, allowing organisations to deploy AI in production successfully and focus on their core business.
The company has a growing list of customers including Standard, Foresight Automotive, Descartes Labs, and Transenterix.
Main image credits: Dataloop