Reducto vs a2z Radiology AI

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

Reducto

LeaderInfrastructure

Cloud Services

SF AI document parsing API processing 1B+ pages monthly at 20%+ higher accuracy than AWS/Google/Microsoft; $108M total ($75M a16z Series B Oct 2025) serving Scale AI, Harvey, and Fortune 10 for enterprise document intelligence.

AI VisibilityBeta
Overall Score
A83
Category Rank
#4 of 85
AI Consensus
73%
Trend
stable
Per Platform
ChatGPT
86
Perplexity
88
Gemini
81

About

Reducto is a San Francisco-based AI document intelligence company — backed by $108 million in total funding including a $75 million Series B led by Andreessen Horowitz in October 2025, plus a $24.5 million Series A from Benchmark in April 2025 and an $8.4 million seed from First Round Capital, Y Combinator, BoxGroup, SV Angel, and Liquid2 in October 2024 — providing enterprises and AI development teams with the most accurate document parsing API available for extracting structured data from PDFs, scanned documents, spreadsheets, and unstructured files at human-level reading accuracy. Reducto processes over one billion pages monthly for thousands of customers including Scale AI, Harvey, Rogo, Fortune 10 enterprises, global financial institutions, and Big Four accounting firms — delivering 20%+ higher extraction accuracy than AWS Textract, Google Document AI, and Microsoft Azure Form Recognizer.

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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