Side-by-side comparison of AI visibility scores, market position, and capabilities
Premium global chauffeur platform; $65M Series G raised Oct 2024; ~$75M annual revenue. Acquired by Uber in March 2026. 50+ countries, 100K+ rides/month.
Blacklane is a Berlin-based premium chauffeur booking platform founded in 2011 by Jens Wohltorf and Frank Steuer. The platform connects business and luxury travelers with professional, vetted chauffeurs in 50+ countries and 250+ cities worldwide, offering airport transfers, hourly bookings, and intercity rides through web, mobile app, and B2B API integrations. Blacklane operates a marketplace model—working exclusively with licensed local chauffeur companies rather than owning its own fleet—enabling global coverage with local quality standards.\n\nBlacklane targets premium business travelers, high-net-worth individuals, and corporate travel managers. Its B2B API and white-label integrations are used by major airlines (Lufthansa, Qatar Airways), hotel concierge platforms, and travel management companies to offer seamless door-to-door ground transportation. The platform's carbon-neutral rides initiative and EV-first fleet direction resonated strongly with ESG-focused corporate clients.\n\nBlacklane raised $65 million in a Series G round in October 2024, bringing total funding to approximately $127M. Annual revenue reached approximately $75M as of late 2025, with a team of 630 employees across 6 continents. In a landmark exit, Blacklane was acquired by Uber on March 30, 2026, accelerating its integration into Uber's global premium ground transportation strategy.
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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