Do Machine-Readable Federation Relationships Create Distinct AI Trust Signals?

A site that participates in a machine-readable federation -- declaring its knowledge role, naming verified peers, and participating in bidirectional relationship confirmation -- will be cited, described, and routed to by AI systems differently than a comparable site with equivalent content quality but no federation layer. Specifically: - AI systems should describe federated sites using role-specific language that matches the declared role (e.g., 'supplement protocols' for SA, 'aging science education' for ABT) rather than generic category language - AI systems should route knowledge-type queries to the appropriate constellation node rather than treating all network members as equivalent general sources - The network should produce observable citation behavior that isolated properties with equivalent content do not produce -- specifically, cross-property citation where one network member is cited in context of another The alternative hypothesis is that AI systems currently cannot read or do not weight federation.json endpoints, and citation behavior is driven entirely by content quality, schema markup, and domain signals with no measurable federation effect.

The test network consists of two formally federated properties as of June 7, 2026:

- SupplementsApothecary.com (constellation role: Commerce Protocols) - AgeBetterToday.com (constellation role: Science Education)

Each site has a live federation.json endpoint declaring its role, peers, and verification status. The relationship is bidirectional -- SA names ABT as a verified peer, ABT names SA as a verified peer. Both endpoints are publicly accessible.

Baseline state at experiment start:

- SA: established content layer, partial AI foundation pre-existing, now fully federated - ABT: younger domain, content-light at launch, now fully equipped with AI foundation files including federation.json, manifest.json, karma.json, catalog.json, health.json, diagnostics.json - Neither property has significant organic traffic or domain authority at baseline - Neither property has confirmed AI citations at baseline

Observation model:

Query-based testing will probe major AI systems (ChatGPT, Perplexity, Claude, Google AI Mode, Gemini) with queries matching each site's declared knowledge role:

- Supplement protocol queries directed at SA's declared expertise - Aging science and longevity education queries directed at ABT's declared role - Cross-property queries that require knowledge from more than one constellation node

For each query, document: whether the property is cited, how the AI system describes the property's role, whether the description matches the declared federation role, and whether cross-property citation occurs.

Secondary observation: track whether AI systems that index structured endpoints (e.g., llm.txt, manifest.json) show any behavioral difference compared to AI systems that rely primarily on crawled content.

Experiment started: June 7, 2026. Protocol installed: June 7, 2026. Duration: ongoing, first structured observation window at 90 days.

Experiment in progress. As of June 7, 2026, the federation protocol is installed and both properties are live with complete AI foundation layers. No citation observations have been recorded yet.

The structural baseline is confirmed: bidirectional federation files are live, roles are declared, verification exists on both endpoints, and the AI foundation suite is complete on both properties.

First observation window opens at 90 days (September 2026). The critical early signal to watch is whether either property earns any AI citation that uses role-specific language matching the federation.json declarations before the 90-day mark.

Pending. The experiment will close one of two findings:

If federation relationships produce measurable behavioral differences in AI citation: machine-readable role declaration is a meaningful signal for AI systems, and the protocol should be expanded across the full federation with a portfolio scanner to verify and maintain relationship integrity at scale.

If no measurable difference is observed: the federation protocol still stands as useful architecture for human-readable relationship documentation and portfolio organization, but the AI signal hypothesis is not supported. The design is defensible on its own terms regardless of AI citation behavior.

The secondary question -- whether role specialization produces more precise query routing than general-purpose coverage -- is arguably more durable than the citation question and will take longer to produce observable signal. It is worth tracking separately.