Case Studies

From Invisible to Citable: An Academic's AI Visibility Score, 3 to 62

AISearch Global's first client engagement: an AEO audit and remediation on a UK academic researcher and published author's personal website. Scattered, unattributed publications with no structured data became a citable, schema-rich archive - AI Visibility Score up 59 points, from 3 to 62. Anonymised case study with intake findings, remediation checklist, and client testimonial.

• Starting problem: No personal website; scattered, unattributed publications; no schema; scored 3/100 at intake.
• Work performed: A full website build, publications archive, attribution and schema work, and a CMS handover guide - more than the standard audit (see the case study for what was audit vs. additional implementation).
• Who implemented it: AISearch Global built and implemented the site directly for the client - this was not client self-implementation.
• Measurement dates: Delivered over a few weeks; score reported 1 September 2026, after a settling period.
• What improved: AISearch Global's own AEO Score Calculator reading rose from 3 to 62 (proprietary rubric, not an external/universal metric).
• What this does not establish: That the standard $1,500 Founder Audit alone produces this outcome. A full website rebuild is additional, separately scoped work outside the audit.

Read Article →

Client Zero Update: 100/100 Structural, 16% Visible

Our structural AEO score hit 100/100, so that lever is now fully pulled. Continuous monitoring over the following nine days shows AI visibility up from 9.4% to 16.0% and average citation position up from 7.0 to 1.5. It also shows that all six unbranded prompts we track still return zero, and that we appear in none of the seven third-party agency listicles AI engines are citing. Here is the full data, including the measurement artefact we refused to report as a win.

• Starting problem: Structural score already high (91/100); whether that translated into actual AI-search recommendation was untested.
• Work performed: Continuous third-party AI-search monitoring (Promptwatch) layered on the completed structural work, including an entity-name confound found and disclosed mid-analysis.
• Who implemented it: AISearch Global, self-audited on its own brand (Client Zero) - not a client engagement.
• Measurement dates: 24 July to 2 August 2026 (a nine-day monitoring window).
• What improved: Structural score 91→100/100 (own rubric); AI visibility 9.4%→16.0%; citation rank 7.0→1.5 (third-party monitor).
• What this does not establish: That unbranded AI recommendation improved. Every unbranded prompt still returned zero visibility, the brand appeared in none of 7 tracked agency listicles, and the window is nine days on one instrument with a disclosed measurement confound.

AISearch Global: Client Zero - From 19 to 91, and What AI Still Doesn't Know

We ran our own AI Visibility Audit on ourselves. Structural AEO score went from 19 to 91 in about a month (20 May to 20 June 2026). AI brand perception - what ChatGPT, Perplexity, and Gemini actually say about us - is a different story. Here is what we found, and why the gap matters.

• Starting problem: A brand-new site launched 20 May 2026 with no content backlog, no schema, and no AI entity history - structural score 19/100.
• Work performed: Full build-out of the AEO Traction Stack - entity clarity, schema markup, answer-format content, and citation-consistency groundwork.
• Who implemented it: AISearch Global, on its own brand (Client Zero) - not a client engagement.
• Measurement dates: 20 May 2026 to 20 June 2026 (about a month).
• What improved: Structural AEO score 19→91/100, on AISearch Global's own rubric.
• What this does not establish: That AI systems recommend the brand. AI Brand Perception scores were low (36/30/41) at this stage, and the later August update found unbranded AI-search visibility still at zero - a high structural score is not the same as AI recommendation.

We Scored Australia's Biggest Plumbing Companies on AI Visibility. Here's What We Found.

One plumbing business asked why they never show up when a customer asks AI to recommend a plumber. To answer it, we scored 88 of Australia's 100 largest plumbing companies against a 20-signal AEO framework. The average was 68.8/100, 89% had no on-page statistics, 83% had no quotations, and being a bigger company didn't help. Here's what the data shows, plus the free hard-water tool that came out of the research.

Read Article →