The Trust Stack: A System for Digital CredibilityThe Trust Stack: A System for Digital Credibility

Making credibility something you can see, measure, and build on.Making credibility something you can see, measure, and build on.

Digital trust isn’t built on brand reputation alone. It’s formed in the moment through experience.

As AI content grows, people increasingly hesitate, double-check, and second-guess what’s real, relevant, or reliable. The Trust Stack is a five-layer system for designing credible digital experiences for humans and machines.

This content reflects an evolving Trust Stack system using a hybrid human–AI evaluation process. Patent filings in progress.

Click on the circle or the layers below.Click on the circle layers to explore the Trust Stack.

Layers of the Trust StackFive Layers of the Trust Stack

View trust impact View marketplace

Designing for humans. Encoding for machines.

Each layer strengthens what people perceive and what models can parse.

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 visible and understandable	Machine-readable disclosures, logic, and permissions expose policy and control
Verification	Claims are supported by visible evidence, not assumptions.	Authenticated sources, citations, and identity signals confirm accuracy and reduce uncertainty

Provenance

Human signal

People see clear origin, authorship, and accountability

LLM signal

Structured metadata enables models to trace, index, and validate source identity

Resonance

Human signal

Tone, context, and content align with intent and situation

LLM signal

Clear semantics, stable entities, and intent signals allow accurate interpretation.

Coherence

Human signal

The story holds true over time and across channels

LLM signal

Consistent narratives, entities, and structures enable cross-context understanding.

Transparency

Human signal

Intent, system behavior, and choices are visible and understandable

LLM signal

Machine-readable disclosures, logic, and permissions expose policy and control

Verification

Human signal

Claims are supported by visible evidence, not assumptions.

LLM signal

Authenticated sources, citations, and identity signals confirm accuracy and reduce uncertainty

Machine legibility makes the Trust Stack operational.

We help teams structure digital experiences so credibility signals are clear to both people and machines — enabling confident decisions, accurate interpretation, and real-time trust.

When the Trust Stack is most useful

The Trust Stack is for leaders building digital experiences where trust is no longer automatic and must be carried by the experience itself.

It applies when products are automated, AI-mediated, or abstract, when users hesitate despite strong offerings, or when security and brand equity exist but confidence still breaks down at the moment of interaction.

This most often shows up in AI-driven platforms, regulated industries like finance and health, and established brands navigating new digital and algorithmic environments. The underlying challenge is the same: credibility must be visible, legible, and verifiable in real time.

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The Trust Stack: A System for Digital CredibilityThe Trust Stack: A System for Digital Credibility

Layers of the Trust StackFive Layers of the Trust Stack

Designing for humans. Encoding for machines.

When the Trust Stack is most useful

Machine-Readable Declaration