The funding will accelerate development of the first scalable AI platform to automatically diagnose and fix data and model issues in computer vision, vital for enabling real-world model adoption
LatticeFlow, the only artificial intelligence (AI) platform that can automatically find and fix AI data and model errors, announced a $12 million Series A funding round. The investment will enable LatticeFlow to expand the capabilities of its platform and respond to growing customer demand as more companies – including a number of Fortune 500 customers including Siemens Mobility, and AI scaleups such as Intenseye, Voxel AI, and Carscan – deploy computer vision models at scale.
The investment in the award-winning ETH Zurich spin-off, bringing total funding to date to $14.8 million, was led by Atlantic Bridge and OpenOcean, with participation from FPV Ventures and existing investors btov Partners and Global Founders Capital. The potential addressable market for computer vision is sizable due to its rapid adoption rate from manufacturing, healthcare, retail, security, and safety industries that are digitizing processes to become more data-driven.
In the past few years, computer vision AI models have surpassed human-level performance across image classification, detection, and other tasks in the lab. However, models often fail to work as expected when deployed in production because real-world scenarios are far more complex and varied than lab training datasets. Because of this, 90% of all models don’t reach production, resulting in billions of losses.
“LatticeFlow is an enabling technology that empowers engineers and companies to deliver quality data and performant computer vision models that work in the real world,” said Petar Tsankov, Co-founder and CEO, LatticeFlow. “As data and models grow, delivering AI models that work in the wild becomes an unwinnable battle, so we built the first smart platform that empowers engineers to accomplish this task, addressing a major pain point.”
“The painful truth is that today, most large-scale AI model deployments simply are not functioning reliably in the real world”
Automating Fixes to Data and Model Issues
The LatticeFlow platform was built to automate the process of solving data quality and blind spot issues in computer vision AI models, critical to enabling model performance in the wild.
Data issues: LatticeFlow is unique in its ability to automatically discover and fix data quality issues at scale across datasets of millions of images, including labeling errors, poor-quality samples, data biases, and others.
Model blind spots: The platform also automates the discovery of blind spot scenarios, often impossible to spot manually, and fixes them before real-world performance is impacted. To patch the model, LatticeFlow has developed a new, scalable method for targeted data augmentation.
“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.”
Ekaterina Almasque, General Partner of OpenOcean said: “If there’s one group that can make machine learning deployments at scale finally happen, it’s LatticeFlow’s team. We were hugely impressed by their amazing pedigree from academia, ETH Zurich, as well as their background as serial entrepreneurs.
“There’s a major bottleneck in bringing AI models built in the lab into production. Despite the exponential growth of AI models, operationalizing them is extremely hard. LatticeFlow is addressing this in a unique way with its unstructured data quality analysis and improvement. We’re excited to join the LatticeFlow journey to build a leading automation platform for computer vision deployments, thus accelerating the roll-out of AI.”