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Army Awards TurbineOne Contract for AI-Powered Edge Target Recognition

TurbineOne announced today it has been awarded a five-year, IDIQ contract with a $98.9M ceiling from the U.S. Army to deliver and demonstrate its Frontline Perception System (FPS) as part of the Army’s Intelligence Enterprise modernization.

Founded in 2021, TurbineOne successfully completed a Phase II Small Business Innovation Research (SBIR) contract which led to this SBIR Phase III IDIQ, a milestone reached by only a fraction of small businesses. In addition to successful completion of a Phase II SBIR, TurbineOne earned a Defense Innovation Unit (DIU) “success memo” for prototype performance, a key factor in the Army’s decision to award this contract.

TurbineOne will deliver AI/ML-driven automated target recognition, counter-UAS, and collaborative autonomy capabilities to accelerate intelligence and targeting cycles, including processing, exploitation, and dissemination (PED) to Army units. With FPS, warfighters can build, retrain, and deploy custom machine learning models at the edge without coding, leveraging multiple sensor feeds in degraded communications environments.

“We are grateful to partner with innovative Army units that pushed us through years of iterative exercises to deliver a software-first, hardware-agnostic capability ready for global distribution and rapid scale,” said Ian Kalin, CEO of TurbineOne. “We look forward to supporting the Army’s Transformation Initiative in collaboration with the innovative intelligence leaders in the Army G-2 and Program Executive Office-Intelligence, Electronic Warfare & Sensors.”

About TurbineOne

TurbineOne delivers AI for the frontlines. Headquartered in San Francisco, TurbineOne’s Frontline Perception System (FPS) delivers real-time decision-making support to America’s warfighters. Trusted across every department in the U.S. Department of Defense and deployed globally, TurbineOne delivers intuitive, mission-ready software that works in the harshest conditions.

With FPS, warfighters can build, retrain, and deploy custom machine learning models at the edge without coding, leveraging multiple sensor feeds in degraded communications environments.

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