SHAIVI^™

SHAIVI is patent-pending attribution infrastructure for AI-generated value. It addresses a question that becomes harder the longer a system operates: when value is produced with machine assistance, whose contribution produced it, and how is that answer preserved once the systems involved have changed.

Economic output increasingly emerges from combinations of human and machine contribution, and the record of who contributed what is the first thing lost. Contribution is diffuse, it accrues across organisations and systems, and it is rarely captured at the moment it occurs. Attribution attempted afterwards is reconstruction, and reconstruction does not survive the scrutiny that rights arrangements eventually attract.

SHAIVI maintains the causal relationships between contribution and value, so attribution can be established rather than estimated, allocated retroactively where the picture only resolves later, and preserved as the systems that produced the value are replaced. Attribution is treated as a durable structure rather than a report generated at a point in time.

Rights are only as strong as the record supporting them. Infrastructure that keeps the causal chain intact is what allows value arrangements to hold across the lifetime of the systems involved — which is the timescale on which these questions are actually contested.