Intent enables machine learning models to learn from real-world behavioural data without processing or transmitting personally identifiable information. Known outcomes such as churn, fraud, or high-value conversion can be linked to behavioural traits on-device, where anonymous training pairs are generated and encrypted before anything is transmitted. Central systems learn from those anonymous behavioural patterns rather than from personal records. The result is a continuous learning system that improves with scale while maintaining a zero-PII architecture by design.
Why it matters:
- Models learn from behaviour without processing PII
- Anonymous training pairs generated and encrypted on-device
- Preserves utility of centralised training
- Zero-PII architecture that improves with scale