Centaur.ai
Centaur.ai provides an AI data platform that combines human and AI labeling signals based on demonstrated performance to produce higher-quality training and evaluation datasets than any single annotator. The platform supports dataset design, mobile-first labeling tools, and delivery via API, JSON, CSV, or DICOM, with proven use in healthcare settings like Brigham & Women's Hospital.
- Artificial Intelligence
- Healthcare Technology
- Software Only
Funding
Funding not disclosed
Founders
Founder details are not available yet.
Product
Problem
Building high-quality training and evaluation datasets for AI models is time-consuming and costly, especially when relying on individual human annotators or traditional labeling services. Individual annotators—whether human or AI—have varying levels of accuracy, and combining their outputs without performance-based weighting can degrade dataset quality, slowing model development and limiting real-world performance.
Solution
Centaur provides an AI data platform that designs, evaluates, and combines human and AI labeling signals to produce production-ready training and evaluation datasets. The platform benchmarks annotator performance, then blends the strongest outputs to exceed individual accuracy. Users can bring their own raw data or work with Centaur to source it, and the platform supports dataset design tailored to specific problems. Production-ready data is delivered via API, JSON, CSV, or DICOM, ready for direct model training and evaluation. The platform also provides rich information on labeler disagreement, which is critical for publishing research results and understanding model limitations.
Target Audience
Primary customers are AI/ML teams in healthcare and research settings, including hospitals, medical device companies, and data science groups that need high-quality labeled datasets for model training and evaluation.
Features
- Performance-based signal combination that weights human and AI annotators by demonstrated accuracy to outperform any single annotator
- Dataset design services, including sourcing raw data when clients do not have it
- Delivery via API, JSON, CSV, or DICOM for direct integration into model training pipelines
- Mobile-first labeling tools that enable rapid, convenient data collection and near real-time model iteration
- Rich labeler disagreement analytics that support research publication and model debugging
- Recruiting capabilities for specialized labeler populations, including medical experts, to meet unique project requirements