Y1 Digital · Internal Session · Aug 2026

AI Visibility

How brands get found when nobody searches anymore

40 model answers · pink: the ones that name your brand

Cold open. The grid is the pitch: forty sampled model answers, and only the pink ones name the brand. Visibility is a rate, not a ranking.

Discovery is collapsing into the answer.

Frame the shift: ten links became one answer. A brand that is not in the answer does not exist for that user.

RECORDED LIVE RUNS · OUR MEASUREMENT INFRASTRUCTURE · TEMP 0

“What is the best online shop for bikes and bike parts?” (EN)

GPT-5.5
Claude Sonnet 4.6
Gemini 2.5 Flash

R · replay

Same prompt, same instant, three answers. Only one names us. Press R to replay the three panels live, safe to repeat.

The model decides, not your website alone.

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welt.de citations on ChatGPT (Agora study, 4,570 citations)

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welt.de citations on Claude (same study, same prompts)

Axel Springer × OpenAI licensing deal, Dec 2023.

Why this matters for Y1: three perspectives

Client brandsAre our clients visible where buying decisions now happen? (GEO scans)
Products in contextWhich product does the model recommend for which situation? (CPIM)
Y1 itselfWe measure. We don't guess. That is the positioning.

Land the three angles: client brands, products in context, and Y1's own positioning as the ones who measure.

An answer is a pipeline, not a page.

Transition into frameworks. An answer is produced by a pipeline, so optimization has to address a stage, not 'the AI'.

The Four Stages

keys 1-4 or click · select stage

Step through the stages with keys 1 to 4. Each stage has its own tactics and its own failure modes.

The GEO periodic table 33 tactics · geo.y1.studio · interactive, click and scroll inside

Live embed, no key needed. Click an element to open a tactic, or just scroll and narrate.

On-table vs off-table: two levels decide

Off-table · the layer above your website Licensing deals · retrieval backends · syndication decides how often you actually appear, and in which model
On-table · your website and content the 33 periodic-table tactics: they decide whether you can appear at all

welt.de, resolved: 264 citations on ChatGPT vs 0 on Claude is not a content problem. It is an off-table licensing deal (Axel Springer × OpenAI).

Resolve the welt.de puzzle from slide 4: on-table work explains eligibility, off-table deals explain amplitude.

Unbranded vs Branded

Unbranded prompts: the discovery KPI
Branded prompts: a token echo, not visibility
Our edge:we report them separately. Most tools don't.

Core methodological point: unbranded prompts measure discovery, branded prompts mostly echo the brand token back. We report them separately, most tools do not.

We don’t audit opinions. We run measurements.

GEO Scan · brandsIs the brand visible in AI answers, and does the domain comply with our GEO framework? Audit against the 33 tactics, plus live measurement per model and language.
CPIM · productsWhich product does a model recommend in which usage context? Product-level, context-specific measurement: the research instrument.

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GEO scans (brand level)

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CPIM pilot (product level)

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≈ measured model answers

The two instruments in one view: GEO Scan for brand visibility and framework compliance, CPIM for product-level context. Everything from here is one of the two.

How a GEO scan works Instrument 1 · brand level

DE EN FR IT ES NL GPT-5.5 Claude Sonnet 4.6 Gemini 2.5 Flash

prompts × models × languages × temp 0 → share of voice

$0

total cost of the five complete scan batches (1,915 answers)

0.7 ct

per measured answer

Instrument 1 mechanics: same prompts, three models, six languages, temperature 0. Cheap enough to rerun anytime.

A real GEO scan report: Lusini reports.y1.studio · interactive, scroll inside

Live embed, no key needed. Scroll within the frame to the section you want, this is the real client-facing report.

Models do not agree: Lusini share of voice by model

Share of voice by model, %

LUSINI on Anthropic: 0%. Amazon on Anthropic: 49%.

Punchline of instrument 1: models disagree massively. LUSINI at zero on Anthropic while Amazon takes 49 percent there.

CPIM: contextual product intelligence (Stihl pilot)

240 prompts

24 contexts

2,400 runs

12,827 mentions

MS 261 C-M 44.7%
MSA 300 29.2%
HSA 56 24.0%
RE 140 0.2%
+88 pp vs competitors · semi-professional construction context −48 pp · beginner first-purchase context (Husqvarna 120 takes 96%)

87% of all mentions were products outside the pilot catalog. The model recommends a wider market than any brief.

Instrument 2, product level: 24 usage contexts, and 87 percent of all mentions were products nobody briefed.

Seven brands later, the patterns repeat.

Act break. From here: what seven brands taught us, pattern by pattern.

Blocked at the Door

verpackgoParent brand named in 94% of answers, the shop itself in just 2%, organically. WAF 403 · GPTBot
LusiniClaudeBot passes; GPTBot and PerplexityBot get challenged: 50,000 items missing from ChatGPT. cf-mitigated: challenge
Lusini / WikidataThe machine record still says “E.M. Group.” Q17539735

Crawler-access failures: verpackgo and Lusini each lose a whole answer surface without noticing.

Present but Wrong, or Absent Where It Counts

Stihl ALLPRONamed in 82% of answers, described correctly in 0%. merged with old AP system
MelittaIts best advisory content is blocked by robots.txt. robots.txt Disallow
Cybex88% share of voice across 10 languages, 49% in English, 0/6 in US buying contexts. en cliff

Content and entity failures: ALLPRO is named but misdescribed, Melitta blocks its best content, Cybex collapses in English.

The 55% Question

Which shop holds 55% share of voice in bike retail?

G, click, or → · reveal

Wiggle.

0%

insolvent since 2023, 90% share of voice on Anthropic

Training data is not reality.

Ask the room to guess out loud before revealing. Press G, or click anywhere on the slide, to reveal Wiggle and the 55% figure.

Three instruments: when to use which

AI Visibility ScanIs a brand visible, where, per model and language? 1-2 weeks, report included.
GEO TacticsThe 33-tactic playbook to fix what the scan finds.
CPIM deep diveProduct-level, context-specific recommendation measurement: the research-grade instrument.

Offer mapping: scan first, tactics to fix what it finds, CPIM when product-level questions matter.

Everything is open.

AI Visibility Scan

ai-visibility.y1.studio

GEO Periodic Table

geo.y1.studio

GEO LinkedIn article

linkedin.com/pulse/word-machine

Every number in this deck traces to a repo artifact. For interpretation, measurement and client projects: start with me.

Leave this up and let people scan. Everything is in the repo and the reports.

Thank you!

Leave this up during Q&A. Contact: Tobias Becker, t@bckr.bio.