A system for evaluating how credibility is expressed across products, platforms, and AI-shaped digital experiences.
Trust is not built through reputation alone. It depends on what people and systems can see, interpret, and act on in the experience itself. The Trust Stack helps organizations evaluate the credibility signals that earn trust across five dimensions: provenance, resonance, coherence, transparency, and verification.
Click the cube or tabs to explore each dimension.
The Trust Stack does not replace model governance, security, legal, or regulatory review. It focuses on a different layer: whether credibility is clear and readable in the experience itself. A system may be technically robust, but if people cannot understand what it is doing, why it is recommending something, or whether it is safe to act on, confidence still breaks. This is the layer the Trust Stack is designed to evaluate.
For the Trust Stack to work in modern environments, credibility has to be legible to both people and machines. The Trust Stack system is built across 5 dimensions, 21 signals, and 125 evidence attributes.
| Trust Layer | Human Signal | LLM Signal |
|---|---|---|
| Provenance | People see clear origin, authorship, and accountability | Structured metadata enables models to trace, index, and validate source identity |
| Resonance | Tone, context, and content align with intent and situation | Clear semantics, stable entities, and intent signals allow accurate interpretation |
| Coherence | The story holds true over time and across channels | Consistent narratives, entities, and structures enable cross-context understanding |
| Transparency | Intent, system behavior, and choices are evident and understandable | Machine-readable disclosures, logic, and permissions make policy and control clear |
| Verification | Claims are supported by tangible evidence, not assumptions | Authenticated sources, citations, and identity signals confirm accuracy and reduce uncertainty |
The Trust Stack is most useful when trust can no longer be assumed from brand familiarity, interface polish, or reputation alone. It applies when products are automated, AI shaped, complex, or high stakes, and when organizations need a more structured way to understand where confidence is forming and where it is at risk.
Before launch, it helps teams evaluate whether credibility signals are structurally sound as products, features, and AI systems enter the market. After launch, it helps identify where confidence weakens in real interactions and what is contributing to hesitation, confusion, abandonment, or loss of trust.
It also supports broader strategic work by helping leadership teams determine where credibility matters most, how it is interpreted across people and systems, and what needs to change across product, content, governance, communications, and operations.