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.
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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.
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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.
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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.
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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.
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