Designing for humans. Encoding for machines.
A system for evaluating whether digital experiences are coherent and credible enough to earn belief, not just attention.
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. It identifies where credibility is holding, where it could erode, and what needs to change before the gap becomes a performance problem.
Before launch, the Trust Stack helps teams assess whether credibility signals are structurally sound as products, features, and AI systems enter the market. After launch, it helps identify where confidence is weakening in real interactions and what is driving 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, communications, governance, and operations.
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 |
When trust can no longer be assumed.
Brand familiarity, interface polish, and reputation still matter. But when audiences trust less, evaluate faster, and AI systems increasingly shape first impressions, those signals are no longer enough on their own. The Trust Stack is built for this: when products are automated, AI-shaped, or high-stakes, and when organizations need a more structured way to understand where confidence is forming and where it is at risk.
Find where credibility is already breaking down before it becomes a performance problem you can no longer reverse.
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 has the greatest impact, how it is interpreted across people and systems, and what needs to change across product, content, communications, governance, and operations.