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Building AI that actually works in healthcare isn’t about clever prompts or bigger GPUs. It’s about proof: Can your model handle the messy edge cases a clinician will see on day one? Can you explain—line by line—why it gave that answer, and who confirmed it’s correct?
That’s the gap Autoblocks and Centaur.AI are closing together.
Individually, we each make your model smarter. Together, we give you an evidence trail a regulator (and your medical director) will actually trust.
Expert-quality data labels from Centaur Labs are consistently more accurate than those gathered using traditional methods—Autoblocks ingests the results automatically.
Push a new prompt or parameter set in the morning; by lunch you’ve got edge-case scores and expert comments.
Every test, every annotation, and every fix is time-stamped and exportable. SOC 2 auditors love us; your legal team will too.
No more “pray and spray.” When the dashboards are green—you go live.
We’re opening a short beta window for teams shipping AI in regulated environments. Beta partners will:
⚡️ The waitlist takes 30 seconds. If “HIPAA” or “FDA” slides are in your next board deck, this is for you.
Speed used to be at odds with safety. Not anymore. Autoblocks ✕ Centaur.AI gives you both—so you can focus on building the future of healthcare instead of firefighting the past.
See you in the beta. Let’s raise the bar together.
Understand why traditional labeling pipelines are hard to scale—and discover how our solution can 10X your pipeline faster, with greater accuracy and efficiency.
Centaur.ai delivers high-quality annotations for neurological datasets where precision determines scientific validity. Through competitive collective intelligence, Centaur produces reproducible labels that strengthen model evaluation and training. NeurIPS attendees working with EEG, EMG, multimodal waveforms, or cognitive modeling should meet with Centaur to see how accuracy is engineered, not assumed.
Explore five predictions shaping AI data annotation in 2026, including the rising importance of data quality, collective intelligence workflows, performance-based evaluation, competitive expert labeling, and annotation as trust infrastructure. This forward-looking analysis highlights emerging trends, practical implications, and strategic considerations for organizations building high-stakes, trustworthy AI systems across regulated industries globally.