We support multimodal data annotation, including radiology (DICOM), pathology, surgical video, audio (e.g., clinical conversations), unstructured text, and tabular data. We annotate complex data across modalities like images, video, text, audio, and waveforms. Click here for a demo of these capabilities.
We source insights from a global network of vetted professionals and students who compete to produce accurate labels. Submissions are validated against gold standards, and only high-confidence consensus labels are delivered. Learn About Our Platform
Unlike traditional vendors, we combine scalable expertise with a competitive quality assurance engine and advanced aggregation algorithms to ensure accuracy, speed, and cost-efficiency. Read Why We’re Different
We serve AI teams across multiple industries. We began with biotech, pharma, digital health, medical devices, and healthcare research and can now serve any industry that is working on AI model development. Use cases include diagnostics, medical imaging, decision support, and mental health applications. Explore Customer Use Cases
We use a contest-driven system with gold standard benchmarking, statistical quality control, and dynamic consensus models to deliver trustworthy labels. See How We Ensure Quality
Our contributors include students, professionals, and subject matter experts vetted for task-specific accuracy and domain knowledge. Read About Our Community
Yes. Centaur.AI is HIPAA-compliant and employs rigorous security protocols to safeguard sensitive healthcare data. See Our Security Practices
Getting started is simple—reach out via our contact form or schedule a discovery call to scope your use case. Contact Us
We offer pilot projects for qualified teams to evaluate our workflows, quality, and turnaround times. Request a Pilot
We scale rapidly to meet your needs, often turning around thousands of annotations daily with flexible team sizing depending on project complexity. Discuss Your Timeline
Yes. We offer tailored workflows and model evaluation services, including annotation-on-error, disagreement resolution, and data re-labeling based on model feedback. Discuss Model Evaluation
