Side-by-side comparison of AI visibility scores, market position, and capabilities
Indoor vertical farming company using AI-optimized growing systems. San Francisco, CA. Raised $940M+ including $400M from SoftBank. Partners with Walmart for US farms.
Plenty is a San Francisco-based indoor vertical farming company that uses AI, machine learning, and robotics to grow leafy greens and other produce in controlled indoor environments. The company has raised over $940 million from investors including SoftBank Vision Fund, which invested $200 million in 2017, and has positioned itself as the technology leader in data-driven indoor agriculture.\n\nPlenty's farms use precisely controlled light, temperature, humidity, and nutrient conditions to grow crops that are free from pesticides, use 99% less land, and consume significantly less water than conventional field agriculture. The company's AI systems continuously optimize growing conditions based on sensor data, learning to improve yields and quality across crops and growing cycles.\n\nIn 2022, Plenty announced a landmark partnership with Walmart to supply leafy greens from a new large-scale facility in Compton, California. This partnership provided both a major commercial anchor and significant additional funding from Walmart, validating Plenty's technology and business model at scale. The company also operates a dedicated strawberry R&D partnership with Driscoll's, the world's largest berry company, demonstrating the platform's potential beyond leafy greens.
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.
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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