The observed-outcomes register is the public log of documented AI retrieval, citation, and feature-extraction behaviour observed on SEO Strategy Ltd client work. Each entry records a dated observation of an AI system’s actual output for a specific query — the surface, the retrieval pattern, the entity treatment, the operational differentiator extracted, the confounders, and the interpretation confidence. The register exists because the strongest evidence for any framework is not the framework’s internal logic. It is documented behaviour observed in the wild, recorded the same way every time, accumulating over months. This page is small at launch. It will compound.
Why this register exists
The AI visibility category is flooded with frameworks, opinions, and predictive claims. Almost no one is publishing structured, timestamped, verifiable observations of actual AI system behaviour. That is the gap this register fills.
Each entry is an observation, not a proof. An observation is a single documented data point from a specific AI surface at a specific time, with the confounders named. A proof would require controlled replication across queries, surfaces, and time windows that no one in this discipline currently has. The honest position is that we have observations consistent with hypotheses about how AI retrieval and recommendation systems behave — and that as observations accumulate, those hypotheses either earn evidential weight or get refuted. Either outcome is useful.
The register is read alongside CITATE (the page-level standard the observed pages were built to) and the AI Discovery Stack (the system model the observations sit inside). The register does not prove either framework. It documents specific outputs in specific conditions and lets readers draw their own conclusions.
How entries are structured
Every entry uses the same fields so that observations are comparable over time:
- Date observed — when the output was captured
- Query — the exact prompt used
- AI surface — which platform and which feature (e.g. Google AI Overview, Google AI Mode, ChatGPT, Perplexity)
- Retrieval pattern — how the source surfaced (cited link, named card, embedded summary, branded panel)
- Feature extracted — the specific operational differentiator the system surfaced, if any, in whose exact wording
- Constraint handling observed — whether the system reasoned about user constraints (timing, location, eligibility) using the source’s structure
- Entity preservation — whether the source was named, linked, logo-displayed, or all three
- Attribution persistence — whether the attribution carried through multiple turns or follow-up queries
- Confounders — what could plausibly explain the result other than the framework hypothesis
- Interpretation confidence — low, medium, or high, with the reasoning named
- Hypothesis generated — what this observation, if it repeats, would suggest
Entry 1: Olliers Solicitors — Operation Soteria
Date observed: March 2026. Client: Olliers Solicitors. Sector: Criminal defence law.
Query: Operation Soteria-related criminal defence query (specific phrasing captured in screenshot evidence held with the client).
AI surface: Google AI Overview.
Retrieval pattern: Citation with the Olliers Solicitors logo displayed alongside the AI Overview answer. The source was named in the answer and visually anchored by the brand mark in the citation panel.
Feature extracted: The page’s structured explanation of the Operation Soteria framework was the substrate for the AI Overview’s answer. The page had been built to CITATE 6/6 with a standalone opening, explicit definitions of the operational terms, attributed claims, and named entity references throughout.
Constraint handling observed: The AI Overview reasoned about the legal context (which police forces, which charge types, which procedural stage) using the structure on the page rather than generic legal definitions. The page’s sectioning by procedural stage was preserved in the AI’s reasoning sequence.
Entity preservation: Named source, linked source, logo displayed in the citation panel. Strong on all three dimensions.
Attribution persistence: Persisted across follow-up queries within the same session. The system continued to surface Olliers Solicitors when adjacent procedural queries were submitted.
Confounders: Olliers is a recognised criminal defence firm with strong off-page corroboration (Chambers, Legal 500, law society listings). The AI Overview citation may reflect that corroboration rather than purely the CITATE-structured page. Disentangling on-page structure from off-page trust is not possible from a single observation.
Interpretation confidence: Medium-high. The combination of named citation, logo display, and structural preservation of the page’s sectioning is consistent with the hypothesis that pages built to extractable structure with strong entity corroboration become reusable substrates for AI Overview answers. It is not conclusive evidence that CITATE structure alone produced the result.
Hypothesis generated: Pages built to a measurement standard (CITATE 6/6) on entities with strong off-page corroboration appear more likely to be selected as the named substrate for AI Overview answers in regulated sectors where citation accuracy carries reputational stakes for the AI system.
Entry 2: Daves Taxis — Cruise Tour Planner
Date observed: May 2026. Client: Daves Taxis (David Plomer, sole trader, West End Southampton). Sector: Local transport / cruise tour services.
Query: Planning-stage cruise tour query for Southampton arrivals with specific docking and sailing time constraints.
AI surface: Google AI Mode.
Retrieval pattern: Branded card display of Daves Taxis with the business named and visually presented as a distinct entity, not merely listed in a results set.
Feature extracted: The AI Mode answer included, in near-verbatim form, the page’s description of the Cruise Tour Planner: “Features a dedicated ‘Cruise Tour Planner’ that filters destinations based on your exact docking and sailing hours.” The operationally distinct feature — the planner that handled the constraint-handling specific to cruise arrivals — was preserved as a named differentiator in the AI’s response. The system did not paraphrase the feature into generic language; it preserved the named tool and its constraint logic.
Constraint handling observed: The AI Mode response sequenced the planning logic (arrival time, docking duration, sailing time, destination selection) using the page’s structured planner as the reasoning scaffold rather than generating generic tour-planning prose. This is the most operationally interesting aspect of the observation: the AI did not just cite the page; it reused the page’s constraint-handling structure inside its own answer.
Entity preservation: Named source, branded card, business presented as a distinct callable entity. Strong on all three.
Attribution persistence: Daves Taxis persisted across multiple planning-context follow-up queries within the same session, including variations in arrival time and tour duration. The system retained the entity and re-applied it to adjacent planning contexts rather than re-retrieving alternatives each turn.
Confounders: Daves Taxis already ranked third organically for “taxi cruise tours Southampton” before the observation, so organic ranking strength is a plausible contributor. The local sector also has fewer competing entities than the criminal defence sector, lowering the bar for entity preservation. Disentangling AI Mode’s feature-extraction behaviour from underlying organic visibility is not possible from a single observation.
Interpretation confidence: High on the feature-extraction observation specifically. The verbatim preservation of “Cruise Tour Planner” as a named tool and the use of its constraint-handling structure in the AI’s reasoning are documented in the captured response. Medium on the broader implication. The hypothesis it generates needs cross-vertical replication before it carries serious evidential weight.
Hypothesis generated: AI retrieval systems may preferentially preserve operationally distinct, named features (planners, calculators, structured decision tools) when those features handle a constraint the user’s query implies. The feature is not just extractable content; it is reusable operational structure the AI can scaffold its own answer around. Generic prose appears more likely to be paraphrased away; named operational tools appear more likely to be preserved verbatim. If this hypothesis holds across further observations, the implication is that high-value pages should increasingly contain named, constraint-aware operational structures (planners, decision flows, calculators) rather than only descriptive prose.
Entry 3: seostrategy.co.uk — H1 Fallback Drift and Recovery
Date observed: 21 May 2026 (ranking collapse identified); recovery tracked through 3 June 2026. Property: seostrategy.co.uk — our own site, documented under the same register discipline applied to client work. Sector: SEO and AI visibility consultancy.
Query: An 11-term canary set tracked in Ahrefs, including “seo strategy consultancy”, “entity corroboration”, “aeo agency southampton” and “wordpress seo consultant hampshire”.
AI surface: Google organic retrieval, including AI Overview features on recovered terms. This entry documents behaviour one layer beneath the citation surfaces in entries 1 and 2: how the retrieval layer classifies a page’s topical authority — the classification every AI surface above it depends on.
Retrieval pattern: Topical authority shift. The homepage H1 was being parsed as the page’s primary topic descriptor, and a change to that single string re-classified the site’s topical scope across the tracked term set.
Feature extracted: The H1 string itself. A PHP template fallback default silently reverted the homepage H1 to a stale value (“B2B GEO Agency”) that diverged from the configured value (“B2B SEO Agency”). One string, two sources of truth, drifted apart without any visible error.
Constraint handling observed: Not applicable in the planning sense used in entries 1 and 2. The relevant behaviour here is classification: the retrieval system re-scoped what the page was about from a single heading change, and ranking positions across 11 terms moved with it.
Entity preservation: The entity itself (SEO Strategy Ltd) remained indexed and identified throughout. What shifted was the topical association attached to the entity — a useful demonstration that entity recognition and topical authority are separate layers that can fail independently.
Recovery observed: The H1 was corrected and the fallback re-architected so the template default and the configured value are now the same string defined in one place — they cannot drift because there is no longer a second copy to drift. By the 3 June 2026 Ahrefs snapshot, 8 of 11 canary terms were at or better than pre-collapse positions, the recovery curve matching crawl cadence after the fix. “Entity corroboration” and “aeo agency southampton” returned to #1 with AI Overview citations; “seo strategy consultancy” gained an AI Overview SERP feature on the homepage; “wordpress seo consultant hampshire” recovered 15 positions.
Confounders: Google’s own algorithm noise during the recovery window cannot be fully isolated from the H1 correction’s contribution. The timing alignment — collapse following the drift, recovery curve matching crawl cadence after the fix — is strong correlative evidence, not proof of sole causation.
Interpretation confidence: Medium-high. The correlation between a single-string change and movement across an 11-term canary set, in both directions, is documented in dated Ahrefs snapshots. What a single incident cannot establish is how much weight the H1 carries relative to other on-page signals.
Hypothesis generated: Silent drift between paired sources of truth — a fallback default and a primary configuration — can re-classify a page’s topical authority without any visible failure, and the damage is invisible until live ranking data reveals it. If this holds, the implication is architectural: fallbacks and configuration should be a single definition, and sites should run proactive scans on invariants (headings, canonical metadata, structured data) rather than waiting for ranking loss to act as the alarm. This incident, caught by live observation, and an earlier metadata drift on the same estate, caught by an automated scan, are the same class of bug detected by two different mechanisms — which is the strongest argument the register has yet produced for systematic invariant monitoring.
What this register is not
This is not a results portfolio. It is not a case study collection. It is not a marketing surface. The register exists to document observed AI behaviour with enough structural consistency that the observations can be compared, the hypotheses can be tested, and conclusions can be drawn or refuted as the entry count grows.
Three entries is a start, not a pattern. Eight to twelve entries across multiple sectors would be enough to begin saying which observations recur, which were idiosyncratic, and which hypotheses are earning evidential weight. The register’s value compounds with entry count and time span, not with any individual observation.
Methodology and update cadence
Each entry is captured at the time of observation: the query, the screenshot, the surface, the response. Entries are added in the order observed. Existing entries are not edited retroactively. If a later observation contradicts an earlier one, both are kept and the contradiction is named in a new entry.
The register is updated as observations occur, not on a fixed cadence. Quality of observation beats quantity of entries.
For the framework standards these observations are tested against, see the CITATE framework, the AI Discovery Stack, and the full SEO Strategy Frameworks register. For the commercial engagement that produces this work, see the AI Visibility Audit.
