[ Our approach ]
How we build training environments for health AI.
We build the gyms, evaluations, and reward signals that let healthcare agents run on real work, get measured against clear criteria, and improve with reliable feedback.
[ Why now ]
Healthcare AI is moving from demos to real work.
Models keep getting better, but healthcare remains one of the hardest domains for agents. Real care delivery is long-horizon, high-stakes, regulated, multimodal, and operationally messy: fragmented records, shifting patient context, billing rules, prior authorizations, clinical judgment, and multi-step work across many systems.
Agents need realistic workflows, expert-defined success criteria, outcome-grounded feedback, and a safe place to train before deployment.
[ What we build ]
Training gyms, evals, and reward signals for healthcare agents.
Healthcare training gyms
High-fidelity environments where agents perform realistic healthcare tasks using the same records, tools, protocols, and workflow constraints they would face in production.
Long-horizon evaluations
Benchmarks that measure whether an agent can complete multi-step healthcare work end to end, not whether it can answer isolated medical questions.
Reward signals and expert feedback
Clinician, operator, and domain-expert judgment converted into rubrics and scoring systems that give models a reliable signal to improve against.
Post-training data infrastructure
Curated trajectories, failure cases, and environment rollouts that support SFT, RLHF, RLVR, and reinforcement learning over tool-use tasks.
[ Some of the workflows we cover ]
[ Who we serve ]
We partner across AI labs and healthcare organizations to ground model development in clinical work that is representative of production.
[ For AI labs ]
Train on healthcare work that looks like production.
Frontier model teams use ChartR environments to find healthcare-specific failures and generate training signal on economically and clinically meaningful tasks.
- Identify healthcare-specific model failure modes
- Improve long-horizon tool use across real systems
- Benchmark agents against real workflow outcomes
- Generate high-quality post-training signal
- Access clinical expertise without building the domain stack in-house
[ For healthcare organizations ]
Help shape safer AI before it reaches your workflows.
Providers, payers, life sciences companies, and specialty groups turn their workflows into training environments without handing over raw data as a commodity.
- Turn real workflows into durable training environments
- Keep domain expertise and compliance requirements intact
- Set the standard your future AI vendors are measured against
- See how agents perform on your work before deployment
- Participate in the upside of the models you help improve
[ How we are different ]
The infrastructure layer for healthcare agents.
Our environments help healthcare AI systems fail safely, get measured, and improve on real tasks so they can complete them end to end.
We are not a data broker.
Raw healthcare data is not enough. The value is in transforming workflows into environments a model can actually learn from.
We are not an annotation company.
Reliability requires expert rubrics, tool-use environments, longitudinal context, and outcome-grounded feedback, not just labels.
We are not another healthcare app.
We build the layer beneath healthcare AI applications: the environments, evals, and training signal that make agents dependable.
We are healthcare-specific by design.
Healthcare is not a horizontal workflow category. Clinical depth, operational context, and regulatory awareness are the moat.
[ Proof points ]
- Environments built across clinical, administrative, and operational healthcare workflows.
- Models trained in ChartR environments have shown measurable improvement on healthcare agent benchmarks.
- Direct partnerships with healthcare organizations to convert real workflows into training and evaluation infrastructure.
- Building toward open, independent evaluation infrastructure trusted by labs and healthcare stakeholders alike.
[ Get in touch ]
Build with ChartR.
Our mission is to build the evaluation and training layer that makes autonomous healthcare AI safe, measurable, and useful. If you are training frontier models or running healthcare operations, we should talk.