Client Zero · Living Case Study
AISearch Global: Client Zero — From 19 to 91, and What AI Still Doesn't Know
Before selling AEO to anyone, we had to prove it on ourselves. Client Zero is AISearch Global's own brand — tracked from day one, 20 May 2026, with every signal, every fix, and every score run exactly the way a paying client's audit runs.
The numbers in this article come from the interactive AI Visibility Audit Dashboard — 10 audit instruments with drill-down panels for every score. Click any signal card to see the full data behind it.
Open the Visibility Audit DashboardStarting From Zero
AISearch Global launched on 20 May 2026. No content backlog. No prior schema. No entity history for AI engines to draw on. A clean baseline by design. If we can't move our own brand's AI visibility using the exact AEO Traction Stack we sell, we have no business selling it to anyone else — so we ran the audit on ourselves first.
Day one score: 19/100 on our own AEO Score Calculator. That's not a humble-brag number — it's a genuinely weak starting position, the same range most Australian SMB sites land in before any AEO work begins.
What We Actually Measure
The AEO Score Calculator checks 20 signals — 17 structural AEO signals plus 3 signals drawn from the Princeton GEO (Generative Engine Optimisation) research. In plain language, they group into the four layers of the AEO Traction Stack:
Entity Clarity
Can an AI engine state, without guessing, who you are, what you do, and where you operate? This is usually the single biggest gap on a new site and accounts for the largest share of early score movement.
Schema Markup
Structured data (JSON-LD) that lets AI systems extract facts directly — your services, your location, your credentials — instead of inferring them from page text.
Answer-Format Content
Content written the way AI engines lift answers: direct questions paired with direct answers, rather than long, undifferentiated prose.
Citation Consistency
Whether your business name, services, and location are described the same way everywhere AI engines can see them — your site, directories, social profiles. Inconsistency erodes trust signals even when each individual mention is accurate.
Layers one and two — entity clarity and schema — account for roughly 80% of the improvement most brands see. That's exactly where our own six weeks of work went first.
The Climb: 19 → 91
Structural AEO score
20 May 2026 → 21 Jun 2026
+72 points in six weeks, built on weekends and evenings around a full-time job. No agency team, no paid sprint. Here's exactly what that work looked like, including the things that went wrong:
- The AEO Score Calculator's own submit button silently reset the form instead of running an assessment — found and fixed mid-build, the kind of bug that would have quietly broken the free tool's entire funnel.
- Deprecated schema (an outdated HowTo block) had to be identified and removed before new, valid schema could go in cleanly.
- Several pages had silent redirect loops from inconsistent internal linking — clean URLs had to be standardised across every page in one pass, not patched page-by-page.
- The nav logo briefly rendered as invisible white text on a white background after an earlier edit — caught and restored before it shipped wider.
- Copy describing the calculator ("13 signals," then "16," then "20") drifted out of sync with the actual scoring logic more than once and had to be reconciled.
None of these were dramatic. They're the ordinary friction of building a structured-data site — and exactly the category of fault an AI Visibility Audit catches before it quietly costs a client citations.
The Score That Didn't Move as Fast
A structural score of 91 tells us the page is machine-readable. It doesn't tell us what AI engines currently know. The second instrument in our methodology — the AI Brand Perception Audit — asks ChatGPT, Perplexity, and Gemini directly, rather than scoring what's on the page and assuming they've read it.
ChatGPT
36/100
Partial recognitionPerplexity
30/100
Limited recognitionGemini
41/100
Partial recognitionA second, related score — GEO AEO Visibility, measuring whether AI engines anchor the brand as Australian and Sydney-based rather than generic — sits at 45/100 ("Emerging Local Authority"), with Local Intent Coverage (31/100) the weakest of its four sub-scores.
Why the gap is real, not a contradiction: 91/100 measures what we control directly — the page itself. 36/30/41 measures what AI models currently know, and those models don't re-crawl and re-learn the web in real time. There's a lag between a page becoming machine-readable and a model actually citing it, shaped by training cutoffs and how often each platform refreshes its citation sources. Fixing the page is necessary but not sufficient — closing that lag is ongoing work, not a one-off fix.
What AI Already Knows
We asked ChatGPT, Perplexity, and Gemini directly: who is AISearch Global, and what do they do? The answers are a useful diagnostic in their own right — not just three numbers.
Getting it right
All three platforms correctly identify AISearch Global as a Sydney-based AI visibility / AEO specialist — not a generalist marketing or SEO agency. Founder name, service category, and Australian location come through consistently wherever the brand is recognised at all.
Still missing
None of the three platforms can cite a specific case study, client result, or independent third-party mention yet. That's the honest shape of a brand-new entity: strong structural signals, no external citation history. Closing that gap is precisely what the Citation Consistency layer of the AEO Traction Stack is built for.
That's the practical meaning behind 36/30/41: the models know what we say about ourselves. They don't yet know what anyone else says about us. This article — and the dashboard behind it — is itself one of the first citable data points working to close that gap.
This Will Keep Changing
Every number on this page is a snapshot. AI platforms update how they crawl, weight, and cite sources on their own schedules — no public changelog, no warnings. A score can shift without the site changing at all. That's why we're running this audit on a fixed cadence, not publishing once and calling it done.
Audit & Update Timeline
Why This Matters for You
Most businesses have neither number. No structural AEO score because no one has run one, and no AI Brand Perception score because the questions have never been put directly to ChatGPT, Perplexity, or Gemini. That means they are making decisions about a gap they cannot see — assuming either that AI is already finding them, or that it is not something worth measuring yet.
The structural gap — the 19/100 range most unoptimised sites start at — is the faster fix. Six weeks of focused work moved ours 72 points. The perception gap is a different clock: AI models do not re-learn the web in real time, so even after a page is fully machine-readable, there is a lag before that model's answers reflect it. The earlier the structural work is done, the earlier that lag starts counting down. Waiting does not pause the gap — it extends it.
The businesses running both instruments in mid-2026 will have a six- to twelve-month head start on the ones who wait until AI search feels urgent. That is the window this article is written into — and it is still open.
Frequently asked questions
Sources
- AISearch Global (2026). Client Zero AI Visibility Dashboard. aisearch.global/insights/client-zero-visibility-dashboard
- AISearch Global (2026). AEO Score Calculator — 20-signal structural assessment. aisearch.global/aeo-score-calculator
- AISearch Global (2026). The AEO Traction Stack — four-layer AI visibility framework. aisearch.global/insights/aeo-traction-stack
- AISearch Global (2026). AI Visibility Audit — service overview. aisearch.global/services/aeo-audit
- Google Search Central (2026). Structured data (JSON-LD) documentation. developers.google.com
- Schema.org (2026). Canonical vocabulary for structured data markup. schema.org
- Aggarwal, P., Murahari, V., Rajpurohit, T., Kalyan, A., Narasimhan, K., & Deshpande, A. (2024). GEO: Generative Engine Optimization. Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. arXiv:2311.09735
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