Train healthcare agents before they touch production
Chartr provides RL environments, simulation frameworks, and world models purpose-built for revenue cycle automation — so your agents fail safely in simulation, not in your clinical workflows.
Healthcare cannot afford to learn from production failures
Revenue cycle and prior authorization workflows are among the most complex, rules-dense administrative systems in any industry. Every failed automation attempt delays patient care and erodes trust in your AI program.
General-purpose RL frameworks were not built for the fidelity healthcare demands. Chartr gives your AI teams the simulation environments that match the actual complexity of payer policies, claim logic, and authorization workflows.
Three layers of training infrastructure
Chartr integrates RL environments, simulation, and world models into a single infrastructure layer for healthcare agent development.
Sandboxed workflow environments
Purpose-built reinforcement learning environments that mirror real payer and provider workflows — claim submission logic, authorization rule sets, denial patterns, and appeals processes — without exposing PHI or touching live systems.
Multi-agent scenario testing
Run thousands of agent episodes against synthetic but statistically realistic scenarios. Stress-test edge cases — high-denial payers, complex auth chains, appeal escalation — before your agents see a single real transaction.
Synthetic healthcare data generation
Generative world models trained on administrative healthcare data patterns produce realistic synthetic patient journeys, payer responses, and claim trajectories — giving your AI teams the training signal they need without real PHI.
From environment to deployment
Define your environment
Configure the clinical and administrative workflow parameters — payer mix, authorization rule sets, claim types, and failure modes. Chartr's environment library includes pre-built templates for common prior auth and RCM scenarios.
Train in simulation
Run thousands of agent iterations against synthetic but statistically realistic scenarios. The simulation framework captures the full state space of real workflows, including edge cases your agents will eventually encounter in production.
Deploy with confidence
Export validated agent policies with built-in safety gates. Chartr's graduated rollout framework lets you expand deployment incrementally, with continuous monitoring against the same simulation benchmarks used during training.
Built for the workflows that matter most
Automate the $13B bottleneck
Train agents to navigate prior authorization submission, status tracking, peer-to-peer escalation, and appeals — across payer-specific rule sets and evolving policy requirements. Simulate denial scenarios before your agents encounter them in production.
Build agents for the full RCM workflow
From claim submission and scrubbing through denial prediction, remittance reconciliation, and underpayment recovery — train specialized agents on every stage of the revenue cycle against environments that reflect real payer behavior and claim logic.
Built for health systems that cannot afford to get it wrong
Healthcare AI carries unique stakes. We built Chartr around the operational and compliance requirements of health system AI teams — not as a retrofit of general-purpose RL infrastructure.
Safety First
Simulation exists so your agents fail in a sandbox, not in your live workflows. Every Chartr environment includes configurable safety gates and failure mode analysis.
Rigor Over Speed
Healthcare demands evidence, not demos. We build infrastructure that supports methodical validation before any agent policy is authorized for production deployment.
Infrastructure Thinking
We build leverage, not one-off solutions. Chartr's environments, simulation framework, and world models are reusable assets that compound in value as your agent program grows.
Transparency
Health systems must be able to understand and audit what their agents are doing. Chartr's training infrastructure produces explainable policies, not black-box outputs.
Ready to train agents the right way?
We're currently running a limited pilot with health systems and AI teams. Tell us about your use case and we'll be in touch.