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Striveworks Raises $33 Million in First Institutional Round led by Centana Growth Partners

Striveworks, a leading provider of machine learning operations (MLOps) for enterprise data science and analytics teams, announced the successful closure of their first institutional fundraising round of $33 million. The all-equity round was led by Centana Growth Partners, a specialized growth equity firm that invests in fintech and related enterprise software, with participation from existing investors.

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“Striveworks has built an impressive platform for continued machine learning that has become a tool of choice for decision makers in industries where effectively leveraging data is equally if not more important than having access to it”

The funding will be used to expand their team, bringing on more engineers, salespeople, and marketing professionals to help drive growth and increase capabilities to better serve their customers.

“At the end of the day, decision makers need to make timely, data-driven decisions. Analytics, sitting under the hood, make that possible, but those decisions and the analytics they draw from need to be measurable, observable, and ethical. At Striveworks, we strongly believe these MLOps processes that keep those analytics performant can and should be done at high speed, behind the scenes and with the lowest user impact possible,” said Striveworks CEO Jim Rebesco. “This new funding allows us to continue to build and refine our industry-leading MLOps platform to support our partners and enable them to efficiently manage the vast amount of data the world has to offer, bringing the platform to where the data and decisions are made. As we look to build on our existing presence by expanding and commercializing in new, highly-regulated industries, partnering with an established firm like Centana, with its own track record of success, was an easy decision.”

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Data analytics has become an essential tool for making effective decisions, but many organizations struggle with keeping production models performant. Striveworks aims to simplify the process with a user-friendly, no-code platform that manages the entire model lifecycle, making it more accessible and effective in fast-paced environments.

Striveworks’ cutting-edge MLOps platform, Chariot, operates invisibly behind the scenes to make the model building, deployment, and remediation process seamless. Striveworks has quickly become a prominent player in the market, with ARR growth of over 300% over the last two years.

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Chariot enables enterprises’ data science and analytics teams to streamline the process of taking machine learning models to production and then maintaining, monitoring, and improving those models. With Chariot, developers can quickly and easily build and train models and then deploy them into production. The platform empowers users with a flexible and differentiated solution offering, including data labeling, model training, deployment, monitoring, and remediation.

Recently, Striveworks unveiled version 4.10 of the Chariot Platform. This release launched Chariot Workspaces, a dedicated environment for users to develop last-mile platform integrations. Additionally, enhancements to Chariot’s NLP and remediation capabilities further enable users to disappear MLOps for a broader range of use cases.

“Striveworks has built an impressive platform for continued machine learning that has become a tool of choice for decision makers in industries where effectively leveraging data is equally if not more important than having access to it,” says Ben Cukier, Partner at Centana Growth Partners. “Our team recognized the immense potential for MLOps platforms to reshape the world of data analytics and quickly took note of Striveworks as a leader in the field. With deep expertise investing in data intelligence and AI, we’re proud to work with and support their exceptional leadership and team as they continue to grow and expand their business.”

“As near peers continue to invest heavily in frontier technologies, it is more important than ever that innovative American companies continue to provide advanced solutions that will enable U.S. and Allied national security to remain best in class,” said 24th Secretary of the U.S. Army and Striveworks Board Member, Ryan D. McCarthy. “Striveworks’ MLOps platform enables decision makers to change the fundamentals of real time decision making and create a distinct competitive advantage. Centana brings the resources of a world class venture partner to expedite product development and scale product capabilities to a very motivated customer.”

The Striveworks founding team has over 20 years of experience working together. With their shared vision of wanting to make MLOps disappear, they started Striveworks in 2018 to combat the issues that plague organizations when it came to their MLOps processes. Striveworks’ board members consist of:

  • Ben Cukier (Centana Growth Partners)
  • Leonard Laufer (Senior Advisor, Blackstone)
  • The Honorable Ryan D. McCarthy (Former Secretary of the Army)
  • Anthony Manganiello (Striveworks)
  • Jim Rebesco (Striveworks)
  • Matthew Zames (Former Chief Operating Officer, JP Morgan Chase)

Striveworks is a pioneer in responsible MLOps for national security and other highly regulated spaces. Striveworks’ MLOps platform, Chariot, enables organizations to deploy AI/ML models at scale while maintaining full audit and remediation capabilities. Founded in 2018, Striveworks was highlighted as an exemplar in the National Security Commission for AI 2020 Final Report.

Centana Growth Partners is a specialized growth equity firm that invests in the future of finance. They collaborate with companies across the entire financial services ecosystem, from institutions to fintech disruptors, to add strategic and long-term value to their portfolio companies. Centana empowers entrepreneurs with a combination of capital, network, and expertise to accelerate their growth. The Centana team taps into decades of financial services expertise and insight to invest in companies that bring real value and innovation to the industry.

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