AI Surface Diagnostic
Aethelstan is a public diagnostic instrument. It evaluates whether a site’s public structural and identity signals support stable, reliable representation by AI systems. Rather than analysing model internals, it examines observable signals and assesses whether they align in ways that reduce ambiguity when processed by machine systems.
How Representation Is Formed
AI systems construct an internal representation of a site by processing its visible structure, links, metadata, and declared identity signals. They do not interpret narrative intent; they compress signals into structured form.
When those signals are consistent and anchored to a clear primary identity, representation stabilises. When they conflict, fragment, or drift across surfaces, representation becomes unreliable or diffuse.
Aethelstan evaluates the structural conditions that influence that outcome.
Identity Declaration
Whether the site explicitly declares what or who it represents, and whether that declaration remains consistent across its visible surface.
Ambiguous or fragmented naming, multiple identity variants, or inconsistent declarations weaken structural clarity. Clear identity anchoring supports reliable representation.
Identifier Stability
Whether canonical URLs, internal references, and declared identifiers resolve to a single, consistent surface.
Multiple canonical forms, inconsistent domain usage, or drifting identifiers introduce structural ambiguity. Stable identifiers allow signals to consolidate rather than disperse.
Structural Hierarchy
Whether document structure supports clean machine parsing.
Headings, sections, and document order communicate logical relationships. A coherent hierarchy enables systems to distinguish primary from subordinate information and reduces interpretive noise.
Surface Coherence
Whether titles, metadata, and visible identity cues align consistently.
Conflicting or drifting surface signals require reconciliation. Consistent surface alignment reinforces structural clarity across layers of representation.
Graph Integrity
Whether internal linking forms a connected and interpretable structure.
AI systems infer relationships from link architecture. Isolated pages or inconsistent internal linking weaken structural integrity. A connected internal graph supports interpretability.
Signal Reinforcement
The strength and consistency with which identity signals are reinforced across the site.
Identity that appears once and dissipates produces fragility. Reinforced identity across meaningful structural positions supports stability and continuity in representation.
Boundary
Aethelstan diagnoses observable structural conditions that influence how a site may be represented by AI systems. It does not access, interrogate, or modify model internals. It does not alter or optimise a site. It evaluates signal presence, alignment, and consistency as they exist.