Side-by-side comparison of AI visibility scores, market position, and capabilities
Metaplane monitors data pipelines and warehouses for anomalies and freshness issues, alerting data teams before bad data reaches dashboards and downstream consumers.
Metaplane is a data observability company founded in 2020 that provides automated monitoring for data pipelines, warehouses, and tables to detect anomalies, freshness failures, and schema changes before they cause downstream problems. The platform connects to data warehouses including Snowflake, BigQuery, and Redshift and automatically establishes baseline metrics for table row counts, column distributions, and update frequency, then alerts data teams when values deviate from expected ranges. Metaplane raised $13M and serves data engineering teams at companies that have invested heavily in their data infrastructure but struggle with silently broken pipelines that deliver incorrect data to business stakeholders. The platform integrates with dbt, Airflow, Fivetran, and Slack to fit into existing data team workflows and provide context-rich alerts that help engineers diagnose issues quickly. Metaplane positions itself as the data equivalent of application performance monitoring, bringing the reliability engineering principles used for software systems to the data infrastructure layer. The company competes with Monte Carlo and Acceldata in the data observability market while targeting mid-market data teams that need observability without the complexity of enterprise monitoring tools.
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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