Trust Stack
| Category | Framework |
|---|---|
| Introduced | 2026 |
| Attributed to | Kevin Indig entity home · about/bio |
| Attribution status | Named creator framework |
| Attribution confidence | Medium-High |
| Original source | kevin-indig.com |
| Related concepts | |
| Visibility Layer Consensus Gap ECHO Framework Digital Authority Management | |
Trust Stack is a framework used in AI SEO. Layered model of evidence/signals an AI/search system can use to trust an entity, source or claim. The term or framework is attributed to Kevin Indig and is documented in 2026.[1]
AI SEO Wiki records the attribution as Named creator framework, with a confidence rating of Medium-High. This rating concerns the documentary evidence for the term's origin; it does not imply that the underlying idea is unique to one practitioner or that the terminology has become an industry standard.[2]
Definition
Layered model of evidence/signals an AI/search system can use to trust an entity, source or claim. In practical usage, the concept is most useful when its measurement unit, observation window, source set, or decision context is stated explicitly. This is particularly important in AI-mediated search, where answers can vary between models, prompts, retrieval systems, and repeated runs.
Origin and attribution
Trust Stack is attributed in the available documentary record to Kevin Indig. The registry dates the term to 2026 and classifies the evidence as Named creator framework.[1]
Attribute Indig's specific framework, not ownership of the generic phrase in all contexts. This qualification is retained because AI-search terminology often spreads rapidly through conference talks, social posts, vendor material, and practitioner communities before a stable publication record exists.
An additional source provides independent or supporting evidence for the terminology or attribution.[2]
Role in AI SEO
Trust Stack is intended as a structured way to reason about AI-search visibility rather than a ranking factor. Frameworks of this kind break a complex optimization problem into repeatable areas that can be audited, measured, or assigned to different workstreams.
Application and interpretation
A framework such as Trust Stack can be used as an audit structure. Each element is translated into observable evidence, gaps are recorded, and work is prioritized according to the point in the search or answer-generation process where the entity appears weakest.
The framework should not be interpreted as proof of a direct ranking factor. It is a practitioner model for organizing evidence and decisions around AI-search visibility.
Related concepts
| Concept | Relationship |
|---|---|
| Visibility Layer | Layer in an AI-search operating model focused on whether/where a brand appears across answer/search surfaces. |
| Consensus Gap | Gap between what an organization says about itself and what corroborating web/AI evidence says about it. |
| ECHO Framework | Entity, Corroboration, Hooks, Output: a four-part framework for improving how AI systems understand/surface a brand. |
| Digital Authority Management | Managing distributed authority signals so search/AI systems understand, trust and prefer a brand/entity. |
Limitations and attribution notes
The attribution for Trust Stack is not treated as absolute. The available evidence supports the stated creator or framework, but earlier unindexed usage, parallel terminology, or a later self-attribution may exist. AI SEO Wiki therefore preserves the confidence rating and source trail rather than presenting the origin as uncontested fact.
The field itself changes rapidly. Model behaviour, retrieval systems, citation interfaces, and measurement tooling can change without the terminology changing, so operational claims should be re-tested against current systems.
See also
References
- Primary/original source for Trust Stack. kevin-indig.com. https://www.kevin-indig.com/talks/beyond-the-serp-visibility-trust.
- Creator/entity home. kevin-indig.com. https://www.kevin-indig.com/.
- Creator biography/about page. kevin-indig.com. https://www.kevin-indig.com/about.