Side-by-side comparison of AI visibility scores, market position, and capabilities
Daasity is an e-commerce data analytics platform for DTC and omnichannel brands, raised $12M+ and based in San Diego, CA.
Daasity was founded in 2017 in San Diego, California and raised over $12M to build a data analytics platform for DTC and omnichannel brands that want consolidated business intelligence without building internal data engineering infrastructure. The company takes a data warehouse-first approach, integrating e-commerce, advertising, subscription, wholesale, and retail data into a centralized data model that powers both Daasity's own analytics dashboards and feeds into business intelligence tools like Looker, Tableau, and Power BI.\n\nDaasity's pre-built data models and connectors are designed around the specific metrics that DTC operators care about: customer acquisition cost by channel, lifetime value by cohort and acquisition source, contribution margin by SKU and channel, subscription churn and retention, and wholesale versus DTC revenue mix. This DTC-specific data modeling dramatically reduces the time brands need to go from raw data to actionable analytics compared to building custom data models from scratch.\n\nDaasity serves omnichannel brands that sell through a combination of their own DTC website, Shopify, wholesale, Amazon, and retail, with the ability to model the economics of each channel consistently. The company competes against TripleWhale, Northbeam, and Polar Analytics in the DTC analytics space, differentiating through its omnichannel scope, data warehouse flexibility, and appeal to brands with analytics-literate teams that want more control over their data modeling than opinionated analytics dashboards allow.
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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