Many AI tools fail after the demo because there is no production discipline behind them.
OraDigit’s MLOps priorities
- Versioned rules and models: every change is traceable.
- Data quality checks: bad inputs create unsafe outputs.
- Monitoring: track usage, errors, latency, and output drift.
- Clinical review loops: improve from workflow feedback.
Deployment discipline applies to both AI models and rule-based clinical workflow tools.