26 October 2022

LatticeFlow Raises $12m to Eliminate Computer Vision Blind Spots

LatticeFlow Raises $12m to Eliminate Computer Vision Blind Spots


LatticeFlow, a startup that was spun out of Zurich’s ETH in 2020, helps machine learning teams improve their AI vision models by automatically diagnosing issues and improving both the data and the models themselves. The company today announced that it has raised a $12 million Series A funding round led by Atlantic Bridge and OpenOcean, with participation from FPV Ventures. Existing investors btov Partners and Global Founders Capital, which led the company’s $2.8 million seed round last year, also participated in this round.

As LatticeFlow co-founder and CEO Petar Tsankov told me, the company currently has more than 10 customers in both Europe and the U.S., including a number of large enterprises like Siemens and organizations like the Swiss Federal Railways, and is currently running pilots with quite a few more. It’s this customer demand that led LatticeFlow to raise at this point.

“I was in the States and I met with some investors in Palo Alto, Tsankov explained. “They saw the bottleneck that we have with onboarding customers. We literally had machine learning engineers supporting customers and that’s not how you should run the company. And they said: ‘OK, take $12 million, bring these people in and expand.’ That was great timing for sure because when we talked to other investors, we did see that the market has changed.

As Tsankov and his co-founder CTO Pavol Bielik noted, most enterprises today have a hard time bringing their models into production and then, when they do, they often realize that they don’t perform as well as they expected. The promise of LatticeFlow is that it can auto-diagnose the data and models to find potential blind spots. In its work with a major medical company, its tools to analyze their datasets and models quickly found more than half a dozen critical blind spots in their state-of-the-art production models, for example.

The team noted that it’s not enough to only look at the training data and ensure that there is a diverse set of images — in the case of the vision models that LatticeFlow specializes in — but also examine the models.

LatticeFlow founding team

LatticeFlow founding team (from left to right): Prof. Andreas Krause (scientific advisor), Dr. Petar Tsankov (CEO), Dr. Pavol Bielik (CTO) and Prof. Martin Vechev (scientific advisor). Image Credits: LatticeFlow

If you only look at the data — and this is a fundamental differentiator for LatticeFlow because we not only find the standard data issues like labeling issues or poor-quality samples, but also model blind spots, which are the scenarios where the models are failing,” Tsankov explained. “Once the model is ready, we can take it, find various data model issues and help companies fix it.”

He noted, for example, that models will often find hidden correlations that may confuse the model and skew the results. In working with an insurance customer, for example, who used an ML model to automatically detect dents, scratches and other damage in images of cars, the model would often label an image with a finger in it as a scratch. Why? Because in the training set, customers would often take a close-up picture with a scratch and point at it with their finger. Unsurprisingly, the model would then correlate “finger” with “scratch,” even when there was no scratch on the car. Those are issues, the LatticeFlow teams argues, that go beyond creating better labels and need a service that can look at both the model and the training data.

LatticeFlow uncovers a bias in data for training car damage inspection AI models. Because people often point at scratches, this causes models to learn that fingers indicate damage (a spurious feature). This issue is fixed with a custom augmentation that removes fingers from all images. Image Credits: LatticeFlow

LatticeFlow itself, it is worth noting, isn’t in the training business. The service works with pre-trained models. For now, it also focuses on offering its service as an on-prem tool, though it may offer a fully managed service in the future, too, as it uses the new funding to hire aggressively, both to better service its existing customers and to build out its product portfolio.

“The painful truth is that today, most large-scale AI model deployments simply are not functioning reliably in the real world,” said Sunir Kapoor, operating partner at Atlantic Bridge. “This is largely due to the absence of tools that help engineers efficiently resolve critical AI data and model errors. But, this is also why the Atlantic Bridge team so unambiguously reached the decision to invest in LatticeFlow. We believe that the company is poised for tremendous growth, since it is currently the only company that auto-diagnoses and fixes AI data and model defects at scale.”