Designing for humans. Encoding for machines.
The Trust Stack is a diagnostic system that helps organizations assess whether public-facing content, communications, and experiences are clear, consistent, supported, and credible enough for people and AI systems to interpret accurately.
It identifies where credibility is strong, where it breaks down, and what to improve to strengthen discovery, engagement, and action.
Click the cube to explore each dimension below.
From Dimensions to Observable Evidence
The Trust Stack does not treat credibility as a loose impression. Each dimension is broken into specific signals, and each signal is assessed through observable attributes that show whether credibility is present, clear, and supported.
The core areas of credibility the Trust Stack examines.
The specific signs within each dimension that can strengthen or weaken confidence.
The concrete features reviewed to determine whether each signal is present, clear, and supported.
Explore the five dimensions
Each dimension turns an abstract trust question into observable risks, signals, and design opportunities.
One system, two outcomes
Differentiation and Discovery
Make your value easier to find, understand, and distinguish from alternatives.
Clarity
People can understand who is behind an experience, what is being claimed, and what matters.
Conversion + Revenue
Increase purchase, renewal, and referral by reducing hesitation at key decision points.
Confidence
People have clearer reasons to believe, compare, continue, or disengage.
Risk Reduction
Reduce avoidable doubt, complaints, competitive confusion, and reputational drag.
Agency
People are better able to verify, decide, and act without relying on unclear, unsupported, or misleading signals.
In AI-shaped environments, credibility has to be clear enough for people to evaluate and structured enough for machines to interpret.
| Trust Layer | Human Signal | AI System 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 |
Where the Trust Stack fits.
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.
See the Trust Stack in action.
An illustrated diagnostic of a customer-facing support experience: where clarity holds, where evidence is missing, and what people and AI systems may misinterpret.
Explore the example →Find where credibility is already breaking down before it becomes a performance problem.
Before launch, the Trust Stack helps teams evaluate whether credibility signals are clear, supported, and structured enough for people and AI systems to interpret. After launch, it helps identify where confidence weakens and what is contributing to hesitation, confusion, abandonment, or loss of trust.