RedBrick AI vs Hermes Robotics

About

RedBrick AI is a healthcare AI platform specializing in medical imaging annotation — providing the configurable workflow system, annotation tools, and quality management features that hospitals, research institutions, and healthcare AI companies need to create high-quality training datasets for medical AI models. Founded in 2021 and backed by Peak XV Partners (formerly Sequoia Capital India) and Y Combinator with $5.1 million raised, RedBrick AI serves customers training AI models on CT scans, MRI, X-rays, and ultrasound images, accelerating annotation workflows by up to 60%.\n\nRedBrick AI's platform provides specialized annotation tools for 3D medical imaging (segmentation brushes for 3D volumes, DICOM viewer integration, measurement tools), configurable review workflows (managing the multi-step annotation, verification, and adjudication process required for medical-grade data quality), and active learning integration (prioritizing the most informative cases for human annotation to maximize model improvement per annotation hour). The platform handles the regulatory documentation requirements for medical AI development, including audit trails that support FDA submission.\n\nIn 2025, RedBrick AI competes in the medical AI training data and annotation market with Scale AI (general-purpose data annotation), Appen, CVAT (open-source), and specialized medical annotation platforms like MD.ai for medical image annotation tools. The healthcare AI market is growing rapidly as FDA-cleared AI diagnostic tools proliferate — each new radiology AI model requires thousands of annotated images for training, and the quality of annotations directly affects model safety. RedBrick AI's medical-specific tooling (3D annotation, DICOM handling, clinical workflow) provides depth that general annotation platforms lack. The 2025 strategy focuses on growing with hospital systems building internal AI capabilities, deepening regulatory compliance documentation, and expanding from imaging to other medical data modalities (pathology slides, clinical notes).