Taccid

Healthcare admin workers spend hours on repetitive paperwork like prior authorization instead of focusing on patients. AI agents can take that on, but there's no good way to test or train them on these workflows. That's what we're building: simulated healthcare environments and testing infrastructure that frontier labs use to benchmark and improve their agents.

These tasks have verifiable answers. "Did the patient try these 5 medications?" "What's the exact primary insurance code?" Outputs can be graded like unit tests, not essays, which makes reinforcement learning possible.

The system has four pieces: a simulated insurance portal (React/Next.js frontend, Python backend, synthetic patient data from Synthea), a task engine where each task is a JSON spec with patient, drug, insurer, and expected outputs scored by Python grader functions, Daytona for spinning up thousands of isolated environments in parallel, and Harbor for standardized packaging so any lab can plug our environments into their training stack.