Simulated patient environments for post-training. Every reward is checked against the clinical guideline, so models learn the standard of care, not shortcuts.
For labs post-training health models. The same environments test any agent before it reaches patients.
Do your agents meet the bar from
Benchmarks test what a model knows. Ordinal tests what it does across a patient’s 30 days, and every reward traces to a written rule.
| Category | Score |
|---|---|
| Round 1 | no data |
| Round 2 | no data |
| Round 3 | no data |
| Round 4 | no data |
| Round 5 | no data |
| Category | Questions |
|---|---|
| Round 1 | no data |
| Round 2 | no data |
| Round 3 | no data |
| Round 4 | no data |
| Round 5 | no data |
Patients hold back facts until the agent asks. Each decision gets a verdict and a reward from a written rule that cites its guideline, the same on every run.
For health systems and agent builders. Compare vendors on your own patients, or check every version before you ship it.
| Agent | Met the guideline | Harmful mistakes | Yearly savings (est.) | Safe to deploy? |
|---|---|---|---|---|
| Vendor A’s agent | 0% | $0.0–0.0M | Checking… | |
| Vendor B’s agent | 0% | $0.0–0.0M | Checking… | |
| Your team today | 0% | Baseline | — |