In the year In 2017, three entrepreneurs – Chris Hazzard, Mike Resnick and Mike Capps – came together to launch a platform to build AI and machine learning tools for the enterprise. Hazard and Resnick have been working on various AI and gaming projects for the US military, while Capps recently retired as president of Epic Games. The aforementioned platform eventually became Diveplane, which today offers products that generate synthetic data to train AI systems, find anomalies in data, and predict market trends.
Demonstrating that its business is healthy, Raleigh, North Carolina-based DevPlan today closed a $25 million Series A round of funding led by defense-focused fund Shield Capital. Calibrate Ventures, L3Harris Technologies and Sigma Defense are also participating. Capps told TechCrunch that the new capital will primarily be used to grow the company’s 20-person headcount and create a new internal department, starting with customer success.
“We have built a platform [easy] Easy to use, fast, transparent, auditable and explainable… We provide the tools for developers and data scientists to fully control data input and production on their hardware or in the cloud. “We like to see our equipment in as many hands as possible, and that’s a big part of this fundraising cause.”
Hazzard, who says DivPlan Technology has a history with government agencies including the U.S. Transportation Command, was founded after working as a software architect at Amazon-owned Kiva Systems and Motorola. Capps met Hazard through an acquaintance and teamed up with Resnick to develop a dive plan verification concept.
Diveplane occupies the MLOps category of AI startups that aim to provide tools to deploy and maintain machine learning models in production. For example, the company’s Gemini product creates anonymized, statistically identical “twin” datasets to train AI systems in a cryptic way. (Training on synthetic data has its downsides, it’s worth noting.) Diveplan’s sonar service, on the other hand, regularly analyzes data and AI systems to make sure the systems don’t drift off course — meaning they become less accurate in their assessments — over and over. time.
“Our technology works with messy data, micro data and small data sets… [a]Our unique single model approach means you train once for any given task, so you can follow the cues in your data, Capps explained. “[I]All are adjustable. [and] Online, so when you need more or different data or bad data that needs to be removed, you can change it on the fly without starting from scratch. If a prediction doesn’t seem right, you can determine exactly how the training data affected the prediction. And they’re all auditable during the lifetime of the model, so you can go back to the system state, re-create the classification, and then pull the full annotation.
On the artificial data side, Diveplane competes with startups like MostlyAI, Gretel and Hazy. And in MLOps at large, it goes head-to-head with rivals such as Arise, Tecton and Weight and Biases, the latter of which raised $135 million last October.
To stand out, DivPlan has focused part of its customer acquisition efforts on defense apparel—reflecting the backgrounds of its co-founders (Capps once taught at the Naval Postgraduate School, and Hazard worked for the Department of Defense).
As L3Harris’ Dan Gittsovich put it in an email: “DoD customers are focused on the use of high-risk AI to improve decision-making speed when lives are on the line, and we believe DivPlan’s deployable and trusted AI solutions can now help combatant commanders.” We’ve also seen DivPlan’s unique and powerful toolset make the AI application attractive to almost any user, so we believe it will provide a discriminating advantage for campaign planning and urgent operational missions as well.
Capps described the rest of DivPlan’s customer base as “larger organizations.” Recently, the startup announced deals with Scanbuy and Mutua Madridena, the largest insurers in Spain.
“[The] The outbreak gave us some headwinds,” Capps said. “We were actively selling into healthcare enterprises, and the pandemic has brought a lot of priorities to those customers and pushed innovation to the back burner. That has changed, and now we’re seeing increased costs coupled with a push for data privacy…regulation is definitely a tailwind for a company like ours.” Because the laws focus on privacy, transparency and accountability, and that’s why we exist!”
