Problem

RedBrick AI addresses the data-preparation bottleneck in radiology artificial intelligence. Radiology AI teams need large, accurate, quality-controlled datasets to train and validate algorithms, but labeling CT scans, MRIs, X-rays, ultrasound, and other medical images is slow and difficult to scale. The company identifies inadequate annotation infrastructure as a barrier to broader clinical AI adoption, while building and maintaining an internal platform can consume engineering resources, create delays, and increase total cost of ownership.

The primary use case is generating reliable ground truth for radiology algorithms, particularly complex 3D studies that require segmentation, multiple readings, review, and consensus. By reducing the time and operational friction involved in annotation, RedBrick AI aims to help healthcare AI companies move more quickly from raw clinical data to trainable, validated datasets.

Product / Service

RedBrick AI offers a purpose-built, web-based SaaS annotation platform for healthcare AI teams. It supports CT, MRI, X-ray, and other medical data, combining a browser-based DICOM viewer with 2D and 3D contouring, pixel-level labeling, project management, task assignment, multi-step review, consensus workflows, and quality-control tools. Its Fast Automated Segmentation Tool uses Meta AI’s Segment Anything Model to accelerate parts of the labeling process.

The platform is designed to fit into existing technical environments rather than requiring customers to move all of their data into a new system. Customers can connect storage such as AWS, Google Cloud, or Azure, integrate with clinical data stores and PACS through APIs, and use SDK or CLI tooling for data operations. RedBrick AI says its customers can retain ownership and control of their patient and annotation data, while its core benefit is faster, more repeatable dataset creation; the company claims teams can generate ground truth up to 60% faster.

Market

RedBrick AI competes in medical-image annotation and radiology AI infrastructure, a specialized segment of the broader healthcare data-labeling and clinical AI software market. Its closest alternatives include open-source tools such as 3D Slicer and ITK-SNAP, as well as annotation systems built internally by healthcare and AI companies. Commercial competitors identified in market databases include TomTec Imaging Systems, Quibim, Advantis Medical Imaging, and Pie Medical Imaging, although the competitive set is broader and includes adjacent medical-imaging software providers.

The company has clear evidence of early commercial traction rather than looking purely pre-revenue. It raised a $4.6 million seed round in 2022 led by Sequoia India and Surge, with participation from Y Combinator and angel investors, and its early customers included Orbem, Prenuvo, Mass General Brigham, and Deeptek. Its website also highlights customer work with Annalise.ai, Qure.ai, Olea Medical, and Radiomics. A 2026 company profile lists RedBrick AI as private and labels its financing-stage records “Generating Revenue,” but public revenue figures, pricing, and current annual recurring revenue have not been disclosed.