Why zkML? Because @OpenText just unveiled its Next-Generation AI Data Platform, built to let AI systems interpret, organize, and act on enterprise information at massive scale. When AI becomes the interface to an organization’s source-of-truth data, verifying how conclusions are derived becomes a core requirement.

2/ This platform doesn’t just store content. It fuses documents, records, and operational knowledge into AI-ready context — powering agents that summarize, classify, extract insights, and drive automated decisions. But as enterprise workflows become AI-interpreted, the challenge shifts from scale to interpretation integrity.

3/ The issue: enterprise AI often functions as a black-box interpreter. Teams see outputs, but not the reasoning behind them: -Which artifacts shaped the conclusion -Whether policy constraints were followed -Whether the correct model version was used -Whether logic drifted outside approved boundaries For operational decisions, unseen reasoning is unacceptable.

4/ That’s where zkML matters: ✅ Prove insights were generated using the approved model ✅Prove which inputs contributed without revealing their content ✅ Prove reasoning chains followed organizational policies ✅Allow auditors to validate outcomes while keeping data sealed zkML transforms AI-generated insights into verifiable enterprise intelligence.

5/ As AI rewires how organizations understand and act on their own data, explainability isn’t enough — proof is required. That’s what @PolyhedraZK is building: workflows you can verify, not just trust.