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We can provide you high-quality, clinically validated training data and the team you need to clean, enhance, or augment your already existing data so that you can focus on building safer, more impactful models.
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The AI models you are building with are remarkably powerful, however we’re seeing that they are not equipped to handle your healthcare needs. Current frontier models can recite the chemical compound of a drug, yet they might fall short of completing an EHR note, triaging a complex case, or producing a prescription the way a trained clinician would. The missing ingredient isn’t additional parameters; it is professional and clinically grounded human feedback at scale.
We see the same pattern repeating across the industry. Cardiac risk models built on male physiology under-flag heart attacks in women. Wearable algorithms confidently tell a 68-year-old with atrial fibrillation that her readings are normal because the training cohort was full of healthy 30-year-olds.
Regulators have stopped accepting "we trained on what we had." Recent FDA guidance and the EU AI Act, which classifies most clinical AI as high-risk now requires demonstrable representativeness, validation, and traceability of the data behind every model.
Carmenta is the data and infrastructure partner for teams building healthcare AI. We deliver production-ready, clinically validated, demographically representative training data and the foundation-model tooling built on top of it. That way your team can focus on the product your customers see, not on the years of data sourcing, annotation, and validation underneath.
We help our customers across two capabilities:
Carmenta works with teams at three points on the healthcare AI curve. Your needs, risk tolerance, and the shape of our engagement are different at each.
Healthcare AI startups (Seed to Series A): You have a product thesis, design partners, and a small, talented team wearing every hat. Your models demo well but fail in real-world edge cases, your iteration cycles are slowed by the cost of sourcing and labeling data, and you cannot justify hiring a full in-house data and clinical-expert pipeline. We become your embedded data and modeling partner: you keep your IP and your customer focus, we handle the data layer underneath.
Frontier model labs building healthcare-capable foundation models: You have world-class researchers and compute capacity, but producing high-fidelity clinical edge cases across specialties, demographics, and modalities is not a problem throwing more GPUs solves. You need expert human signals at frontier scale, with the safety and validation scaffolding that lets you ship a model regulators and health systems will trust. Carmenta supplies the corpus and the expert pipeline behind it.
Late-stage healthcare AI companies scaling across specialties: Your product is in clinical use. Every new specialty, geography, or modality you expand into multiplies the validation surface. Edge cases get harder to anticipate and model failures get more expensive. We provide the continuous, versioned data and evaluation infrastructure that keeps your models safe as they scale.
We sequence engagements to match where you are. Most customers start narrow and expand as trust and scope grow.
1. Data spec discovery (1-2 weeks): We start discussing your data needs, and the gaps you’d like to fill. This is when you can provide us with your spec sheet, delivery method, etc. This would include data formats, size, deadlines, budget, SFT-intent, clinical domain, regulatory requirements, and anything else you’d need and prefer for your RLHF process. Once we understand your needs, we will provide you samples for you to assess.