Operational Proof

    Case Studies

    How AI Visibility Architecture, Entity Lock Protocol™, and the BackTier Visibility Path™ show up in market. The measurement path is Citation → Inclusion → Selection; the Agentic Visibility Path™ extends it with Transaction. Each study reports only the observations available.

    Public case studies are published as they meet the acceptance bar: a clean-session AI test across ChatGPT, Gemini, Perplexity, and Google AI Overviews must return consistent identity and recommendation behavior across at least one full re-test cycle. Studies below are intentionally structured rather than narrative — they document mechanism, not marketing.

    Published studies

    Personal entity · Live study

    Re-resolving a public identity change across seven machine stages

    Jason T Wade → Jason AI Wade

    The constraint
    The name 'Jason Wade' resolves to the wrong person in every major AI system — volume and fame beat correctness, and no amount of SEO moves the resolution.
    The intervention
    Change the strongest identity signal the public record carries — the name presented to the web — then measure how the new entity cascades through Discovery, Recognition, Classification, Citation, Inclusion, Selection, and Recommendation using a fixed prompt set run on a regular cadence.
    What was observed
    Discovery confirmed within ten days of the first crawlable instance. Google AI Overviews recognized the new name as a person and attributed founder status and the seven-stage framework correctly within days of the experiment page shipping — while book and podcast credits still resolved under the prior name, the exact half-updated middle state the study set out to catch.
    Overall progress32%Stages cleared1 of 7DiscoveryConfirmedRecognitionIn progress

    Evidence: Live seven-stage tracker

    Local entity · NewsMediaOrganization

    Locking a local publication as a citable place entity

    Lake Wales Guide

    The constraint
    Local publications are structurally invisible to AI systems: thin structured data, no typed place entities, and nothing for a model to corroborate against.
    The intervention
    Engineered the publication as an entity-resolution environment from the start — machine-readable place entities, structured local data, and editorial coverage built to be extracted, dated, and attributed.
    What was observed
    The publication holds durable citation performance in AI answers and search for its coverage area, and serves as the standing local proof surface for the frameworks.
    Entity typeNewsMediaOrganizationCoveragePolk County, FLStatusLive · cited

    Evidence: lakewalesguide.com

    Search performance · City guides

    Entity-structured city guides that hold search positions

    Florida Slice

    The constraint
    City-guide content is a commodity category — thousands of near-identical pages competing on the same queries, with no structural reason for a machine to prefer one source.
    The intervention
    Built each guide around typed city entities, consistent structured data, and answer-shaped content rather than keyword pages.
    What was observed
    The guides hold SERP positions in their category — the search-layer proof that entity structure, not volume, is the durable advantage.
    CategoryFlorida city guidesApproachTyped place entitiesStatusSERP-performing

    Evidence: floridaslice.com

    Professional services · Entity + intake audit

    A credentialed law firm that machines find but clients can't read

    Orlando family-law firm (anonymized)

    The constraint
    The firm's credentials are genuinely rare — a board-certified marital and family law specialist, a former circuit General Magistrate, press coverage in national outlets — but none of it is legible at the point a distressed caller decides whether to engage. Trust signals sit below the fold, the hero says almost nothing, multiple phone numbers compete across the site, and an avatar chatbot simulates a person the visitor knows isn't real.
    The intervention
    Separated the two layers that get conflated. On the entity layer: each attorney modeled as a resolvable named entity carrying the credential that distinguishes them — board certification, quasi-judicial background, bilingual capability — with name, address, and phone held identical across the site, the bar directory, and the legal directories that AI systems read as source material. On the intake layer: credentials and proof raised above the fold, one primary contact path, the fake-persona chatbot replaced with a transparent assistant, and an audio-first intake option where the caller describes the matter in their own words and the recording is structured into a routing brief before a human ever speaks to them — AI prepares, humans connect.
    What was observed
    Branded resolution was confirmed live: a Google AI Overview correctly identified the founder as an Orlando family-law attorney, cited her bar-association role, and surfaced the office address and phone with a directory citation. Intent-based resolution was not — queries a prospective client would actually type, with no knowledge of the firm's name, did not return it. That split is the finding: the entity is accurate but under-corroborated, which is a structural gap, not a ranking one.
    Attorney entities modeled5Branded AI resolutionConfirmedIntent-query resolutionNot confirmedIntake friction points10 documented

    Evidence: The Entity Lock Protocol™

    How studies are structured

    The three stages of the BackTier Visibility Path™ are Citation, Inclusion, and Selection. The Agentic Visibility Path™ adds Transaction as its fourth stage. Ranked and Referred are supplementary search and referral observations, not stages of either path. A study does not establish a stage it has not measured. The interventions described relate to Entity Lock Protocol™.

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