Diveplane receives $25 million to expand its MLOps platform

In 2017, three entrepreneurs – Chris Hazzard, Mike Resnick, and Mike Capps – came together to launch a platform for building enterprise-focused AI and machine learning tools. Hazzard and Resnick have worked on various AI and gaming projects for the US military, while Capps recently stepped down as president of Epic Games. The aforementioned platform eventually became Diving planewhich today offers products that create synthetic data to train AI systems, find data anomalies, and predict market trends.

In a sign that business is healthy, Raleigh, North Carolina-based Diveplane today closed a $25 million Series A funding round led by defense-focused fund Shield Capital, with Calibrate Ventures, L3Harris Technologies and Sigma Defense are participating. Capps tells TechCrunch that the new capital will primarily be used to grow the company’s headcount of about 20 people and create new internal departments, starting with customer success.

“We built a platform that is [easy] to use, faster, transparent, auditable and explainable… We provide the tools for developers and data scientists to manage data input and output entirely on their hardware or cloud,” Capps said in an email interview. “We’d love to see our tools in as many hands as possible, and that’s a big part of the reason for this fundraiser.”

Diveplane’s technology, which Capps says has a history with government agencies including the US Transportation Command, came from Hazardous Software, the company Hazard founded after working as a software architect at Amazon-owned Kiva Systems and Motorola. Capps met Hazard through a mutual acquaintance and they collaborated with Resnick to develop a proof of concept for the diving plane.

Diveplane occupies the MLOps category of AI startups whose goal is to provide organizations with tools to deploy and maintain machine learning models in production. For example, the company’s Geminai product creates anonymized, statistically similar “twin” data sets to train AI systems in a supposedly privacy-preserving way. (Synthetic Data Training it has its downsidesworth noting.) Diveplane’s sonar service, meanwhile, performs regular data analysis and AI systems to ensure the systems don’t drift off course—i.e. become less accurate in their predictions—over time.

“Our technology works with messy data, sparse data and small data sets… [a]and our unique single-model approach means you train once for any type of task, so you can follow the signal in your data,” Capps explained. “[I]everything is editable [and] online, so when you need more or different data, or find bad data that needs to be removed, you can change on the fly without starting from scratch. If a prediction doesn’t look right, you can track exactly which training data influenced the prediction. And it’s all verifiable throughout the life of the model, so you can go back to the state of the system, re-create a classification, and then download the full explanation for it.”

On the synthetic data side, Diveplane competes with startups like Mostly AI, Gretel and Hazy. And in MLOps more broadly, it faces rivals like Arise, Tecton and Weights and biasesthe latter of which raised $135 million last October.

To set itself apart, Diveplane has focused some of its customer acquisition efforts on defense outfits—reflecting the backgrounds of its co-founders (Capps once taught at a naval graduate school and Hazard worked for the Department of Defense).

As L3Harris’ Dan Gittsovich said via email, “DoD customers are highly focused on the responsible use of AI to improve their decision-making speed when lives are on the line, and we are confident that explainable, reliable AI solutions of Diveplane can help combat commanders now. We also saw that Diveplane’s unique and powerful toolkit makes the AI ​​application intuitive for almost any user, so we believe it will provide a discriminating advantage for both campaign planning and emergency operational missions.”

Capps described the rest of Diveplane’s customer base as “larger enterprises.” The startup recently announced deals with Scanbuy and Mutua Madrileña, one of the largest insurers in Spain.

“[The] the pandemic has created some headwinds for us,” Capps admitted. “We have been actively selling to healthcare enterprises and the pandemic has put much higher priorities on those customers and put innovation on the back burner. That has changed, and now we’re seeing increased costs combined with widespread data privacy pressures… Regulation is certainly a tailwind for a company like ours, because regulations tend to focus on privacy, transparency, and accountability, and that’s the whole reason we exist !”

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