Powin vs a2z Radiology AI

Side-by-side comparison of AI visibility scores, market position, and capabilities

Powin

GrowthEnergy

Battery Energy Storage Systems

Powin delivers utility-scale battery energy storage systems with integrated software for grid operators and utilities managing the clean energy transition.

About

Powin is a battery energy storage system provider founded in 2012 and headquartered in Portland, Oregon, designing and delivering large-scale battery storage solutions for utilities, grid operators, independent power producers, and large commercial customers. The company provides complete turnkey storage systems combining battery hardware, power conversion, thermal management, and its Stack OS software platform for system monitoring, optimization, and performance tracking. Powin has deployed over 6 gigawatt-hours of energy storage across projects in the US, Europe, and Asia Pacific, establishing itself as one of the leading independent BESS integrators in the market. The company is battery-agnostic and works with multiple cell manufacturers to optimize cost and performance for specific project requirements. Powin raised $150M in 2022 to scale operations and expand its manufacturing and project development capabilities. As renewable energy penetration on electricity grids increases, the demand for grid-scale storage to balance supply and demand is growing rapidly and Powin is positioned to capture significant share.

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a2z Radiology AI

EmergingEnterprise AI

Medical Imaging AI

a2z Radiology AI raised $20M in 2025 for its whole-body AI that simultaneously screens for 24+ conditions across CT scans — from incidental cancers to cardiovascular risk — in a single automated read.

About

a2z Radiology AI has developed a whole-body CT analysis platform that simultaneously screens for over 24 medical conditions across a single CT scan, including incidental cancers, coronary artery disease, aortic aneurysm, bone density loss, and organ abnormalities. The AI acts as a second reader that radiologists can use to catch incidental findings that fall outside the primary reason for a scan — a major source of missed diagnoses.

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