Side-by-side comparison of AI visibility scores, market position, and capabilities
Sinai Technologies provides a decarbonization planning platform that models carbon reduction scenarios and tracks abatement progress against net-zero targets for large enterprises.
Sinai Technologies is a climate technology company founded in 2019 and based in San Francisco that has raised $50M to build software for enterprise decarbonization planning and execution. The platform enables sustainability and operations teams to model the impact of different decarbonization initiatives including energy efficiency projects, renewable energy procurement, fleet electrification, and supplier engagement programs before committing resources. Sinai uses a scenario modeling engine that accounts for capital costs, implementation timelines, operational impacts, and emissions reductions to help companies build credible, least-cost pathways to their climate targets. The company serves large industrial companies, utilities, and enterprises with significant capital-intensive decarbonization programs where investment decisions require rigorous analysis of emissions and financial trade-offs. Sinai has built strong capabilities for Scope 3 supplier engagement programs that help companies systematically reduce value chain emissions through targeted supplier outreach and performance tracking. The company positions itself as the planning and execution platform that translates corporate climate commitments into operational programs with accountable owners and measurable progress.
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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