Activepieces vs a2z Radiology AI

Side-by-side comparison of AI visibility scores, market position, and capabilities

Activepieces

EmergingDeveloper Tools & Platforms

General

SF open-source AI-first workflow automation at $1.7M ARR Dec 2024 with 450+ integrations and self-hosted option; YC $1.55M competing with Zapier and n8n for compliance-conscious teams needing MCP/Claude/GPT-4 automation pipelines.

AI VisibilityBeta
Overall Score
D32
Category Rank
#230 of 1167
AI Consensus
61%
Trend
stable
Per Platform
ChatGPT
31
Perplexity
28
Gemini
41

About

Activepieces is a San Francisco-based open-source workflow automation platform — backed by Y Combinator with $1.55 million raised from ByTheTower, Forward VC, Fundamental VC, Kima Ventures, and Soma Capital in November 2024 — providing teams with an AI-first business process automation alternative to Zapier, Make.com, and Workato through a self-hostable, MIT-licensed platform with 450+ integrations, native AI agent support (GPT-4, Claude, Model Context Protocol), and a managed cloud version. Founded in 2022 and reaching $1.7 million in annual revenue by December 2024 with a team of 11-19 employees, Activepieces has differentiated through its open-source model — allowing companies with data compliance requirements (GDPR, HIPAA, SOC 2) to run the entire automation infrastructure on their own servers rather than routing sensitive data through third-party SaaS automation platforms.

Full profile

a2z Radiology AI

EmergingEnterprise AI

Medical Imaging AI

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.

About

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.

Full profile

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