Groundlight Announces Natural Language Powered Computer Vision Service for Manufacturing, Automation and Robotics

Company Backed by Madrona, Greycroft Partners and Founders Co-Op Announces $10 Million in Funding

AI startup, Groundlight,  announced the launch of its visual understanding service based on natural language instructions. Groundlight’s service enables any developer to understand images programmatically using simple English language instructions and just a few lines of code. Groundlight’s platform is easily integrated into applications such as industrial automation, process monitoring, retail analytics, video stream analysis, and robotics. This pilot service is now available to select customers.

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“Is the USPS mail carrier in the lobby”

Groundlight’s innovative platform empowers any developer, even those without data science experience, to quickly build robust vision solutions. Previously developers would have to gather and label a dataset, train a model, and then figure out an MLOps solution to maintain it, a process that could easily take months. Instead, Groundlight lets developers get going in minutes by simply describing their visual task in natural language, which is instantly turned into an application-specific model. Groundlight tracks the confidence of this model and continuously optimizes it using feedback from expert human monitors.

“As the most powerful AI models get better, there is the tendency to think that all you need is a Large Language Model. While they make great demos, robust commercial systems need real human judgment to handle edge cases reliably. And massive LLMs are too slow and expensive to be relied on in isolation for many applications,” said founder Leo Dirac. “Groundlight’s approach seamlessly ties together traditional deep learning with massive foundation models, edge computing, and real-time expert human supervision.”

Groundlight enables small and mid-sized manufacturers to quickly turn on ML solutions that increase productivity. Austere Manufacturing in Washington State produces cam buckles known for their high performance and lightweight quality meant to last a lifetime. “Quality control, process efficiency, and continuous improvement are crucial to our success. We’re excited to use Groundlight to inspect our products and monitor our processes without the development overhead of a typical industrial solution. Their API enables a $10 camera and a few lines of code to implement a working solution in minutes. Far less time than we’d spend to even evaluate an expensive industrial computer vision product,” said Uriel Eisen, Founder of Austere Manufacturing.

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Groundlight is also applicable to companies with large retail and warehouse footprints. Even if these companies have existing solutions, implementing the same ML solution in a new environment often means starting from scratch on data collection and training. Groundlight lets users ask the question they really care about and start getting results immediately, like “Is the USPS mail carrier in the lobby” or “Is there more than one customer waiting in line” rather than sift through a list of potential similar queries and wade through the false alerts from a generic model such as one that detects a person in an environment.

“So many ML projects don’t even get off the ground because of the big effort and scarce expertise required to credibly investigate the feasibility of a solution, much less implement it. Then they require operational expertise to keep them running. Groundlight has the potential to change all of that,” said Thomas Stubbs, ARC Labs, Slalom Consulting.

“The newest generation of foundation models are incredibly powerful, but they are both expensive to operate and unreliable for many use cases. Every company has unique data, especially in industrial applications. Groundlight’s approach enables companies to utilize off-the-shelf cameras and inexpensive equipment to quickly build and reliably operate customized models. We are excited to work with this brilliant team as they build the company,” said Tim Porter, managing director, Madrona.

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