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AI Visibility — Strategy & Audit Hub
ChatGPT, Gemini, Perplexity and Google AI Overviews read a small set of high-authority sources. Brands that exist on Wikipedia and Wikidata are cited consistently. The rest are paraphrased — or skipped.
What you get
AI answer engines (ChatGPT, Gemini, Perplexity, Google AI Overviews) draw on a small set of high-authority sources. We audit your presence across Wikipedia, Wikidata, Reddit, Quora and structured media — and build the missing pieces.
Starting price
Audit from €490
Typical timeline
Ongoing
Best for
This page is the strategy and audit hub — it maps how AI visibility works and what an engagement looks like. For scoped packages and transactional pricing, see the AI visibility packages page.
Terminology
The labels get used interchangeably. They shouldn't be. The one-sentence versions, so the rest of this page reads precisely.
AEO — Answer Engine Optimization
Making your brand citable when an AI engine composes the answer itself — entity and source work, covered in depth on our Wikipedia AEO page.
GEO — Generative Engine Optimization
The research literature's near-synonym for AEO — structuring content so generative engines retrieve, trust and cite it.
SEO — Search Engine Optimization
Earning rank and clicks in a list of links — still necessary, but it works at the page layer while AEO and GEO work at the entity and source layer.
Full comparison, with where each one pays off: AEO vs GEO vs SEO.
Why this matters now
The companies behind ChatGPT, Gemini and Perplexity don't invent their answers — they read, and pay for, the high-trust public web. The data shows exactly which sources they lean on.
~$60M / yr
What Google pays per year for access to Reddit's data to train and ground its AI — a signal of how much these platforms are worth to AI systems.
Source: Reuters, Feb 2024; Reddit S-1.
~21%
Reddit is the single largest source of citations in Google's AI Overviews — roughly 21% of top citations.
Source: Profound, analysis of 680M citations, 2024–25.
#2
Reddit is the second most-cited domain in ChatGPT — behind only Wikipedia.
Source: Profound, 2025.
Citation share varies by query type and shifts over time — so every engagement starts with a live audit of your actual AI footprint.
Why this is happening
LLMs are trained on the open web but answers must be defensible. When a model needs to describe a company, it leans on a small, repeated set of encyclopedic and structured sources. Brands that sit inside that set are quoted accurately. Brands outside it get paraphrased — or hallucinated.
The training layer
Foundation models like GPT-4, Claude, Gemini and Llama are pre-trained on Wikipedia and a handful of structured datasets. Wikipedia alone makes up several percent of every major model's training corpus.
The retrieval layer
ChatGPT browsing, Perplexity, Gemini grounding and Google AI Overviews query the live web. They preferentially surface Wikipedia, Wikidata, high-authority media, and structured Q&A like Reddit and Quora.
The entity layer
Behind every confident AI answer is an entity ID — usually a Wikidata Q-number. If your brand has no entity, AI systems treat you as a string of letters, not a known company.
Trust Visibility Infrastructure
Each layer feeds the next. Coverage makes the entity defensible, the entity gives machines an identity to resolve, community threads carry real buyer questions — and the answer layer is where the result gets measured.
Layer 1
Independent media and structured coverage — the raw material every other layer cites and verifies against.
Layer 2
Wikipedia and Wikidata turn coverage into a machine-readable identity with an entity ID systems can resolve.
Layer 3
Reddit and Quora threads where buyers actually ask — and where AI engines look for candid, current answers.
Layer 4
Where the work pays off: answer engines cite the stack — and we measure whether they actually do.
The AI visibility ecosystem
AI answer engines read from the same set of high-authority sources humans trust. Coverage on each platform compounds — gaps on any one of them are noticeable.
Encyclopedia
160+ language editions, the most-cited reference on the open web.
Why: Heavily weighted by every major LLM. The single biggest source of brand context for AI answers.
Structured data
Open knowledge graph — entities, relationships and identifiers in machine-readable form.
Why: Feeds Google's Knowledge Graph and is read directly by LLM retrieval pipelines.
Community
Threaded discussions across thousands of communities.
Why: AI systems cite Reddit threads as 'real user opinion'. Often shown in Google Search results.
Q&A / AI answers
Q&A platform with expert long-form answers.
Why: Strong organic search ranking and direct citation by AI answer engines.
Structured data
The right-rail card on Google Search results.
Why: First impression for ~80% of branded searches. Powered by Wikipedia + Wikidata.
Q&A / AI answers
Generative AI answer engines.
Why: Where buyers increasingly research brands before clicking through to Google.
Media
Independent media that meets Wikipedia's reliable-source bar.
Why: The notability backbone. Without it, no Wikipedia page is safe from deletion.
Alt-wiki
Simpler editorial standards on the same Wikipedia infrastructure.
Why: Easier publication path that still inherits Wikipedia's domain authority.
How an engagement runs
Step 1
We query the major LLMs about your brand, your competitors, and the questions your buyers ask. We map what's said today and where the gaps are.
Step 2
Based on the audit we propose a coordinated plan — Wikipedia, Wikidata, Reddit, Quora, structured media — sequenced to compound.
Step 3
We execute across platforms, with a single project lead. Every step is source-first and policy-compliant.
Step 4
Monthly checks across the major LLMs. We report how brand mentions evolve and surface emerging risks early.
Typical timeline
The honest version of 'how long does it take' — a typical sequence for a brand with a workable source base, not a guarantee. Engines refresh on their own cycles, so stages overlap and the tail runs long.
Step 1 · Days 1-14
Wikidata entity, structured data and Knowledge Graph eligibility. A Google Knowledge Panel can initiate within days of a clean entity.
Step 2 · Weeks 2-4
The Wikipedia article (where notability holds), structured media and the first community threads go live — independent records that confirm each other.
Step 3 · Weeks 3-6
Retrieval-based engines move first: Perplexity, Google AI Overviews and ChatGPT browsing typically reflect new high-authority records within one to two weeks of publication.
Step 4 · Weeks 6-8+
Monthly checks start logging citation events. Model-trained answers in ChatGPT and Gemini move slowest — often 30-90 days, some only after the next model refresh.
Typical, not guaranteed — pace depends on source readiness, category competition and each engine's refresh cycle.
Routes compared
Three routes to the same goal, priced honestly. The cheap ones are not wrong — the risk just gets billed later, in deletions and absent follow-through.
| Route | Total cost (honest range) | Time investment | Pass-rate risk | Deletion defence | AI-citation follow-through |
|---|---|---|---|---|---|
| DIY | No cash cost — paid in your own time | 40-80 hours: policy, sourcing, drafting, review replies | High — undisclosed conflict of interest and promotional tone are the fastest routes to rejection | None — you argue any deletion discussion alone | None — nobody checks whether AI ever cites the page |
| Freelancer marketplace | $50–$500 per gig; revisions often extra | Low upfront — rework cycles when drafts bounce | 95% of the Fiverr-built articles we tracked in 2024 were deleted within 90 days | Rare — most gigs end at publication | Not offered |
| WikiBusines | €490 audit + from €1,930 per page | A few hours of interviews and fact-checks | 93% success rate — we decline builds the sources can't carry | 90-day monitoring; 80% refund after three defence attempts | Monthly citation checks across the major AI engines |
Marketplace deletion rate from our 2024 observation window — methodology on the notability audit page. Success-rate and refund terms in full on guarantees.
What the audit covers
What we audit
We check how your brand is described across the major AI answer engines, then trace those answers back to the public-source layers each engine retrieves from.
The answer engines
Claude, ChatGPT, Perplexity, Gemini, Bing Copilot and Google AI Overviews — we capture, verbatim, how each one currently describes your brand and your category.
The public-source layers
The layers these engines retrieve from — encyclopedic (Wikipedia), structured data (Wikidata, Knowledge Graph), community (Reddit, Quora) and media — and where your coverage has gaps.
What we control — and what we don't
We improve the public-source layer these systems retrieve from — we do not control what an AI ultimately says.
Why the gaps are costly
Without a reliable public footprint, an AI describes you from blog posts and competitor analyses — or fills the gaps itself.
Free check
Send a brand name. Within 48 hours we run the questions a buyer would actually ask — across ChatGPT, Perplexity, Gemini and Grok — and send back the verbatim answers with the sources behind them.
> Who are the leading providers in [your category]?
The established options include CompetitorA, CompetitorB and CompetitorC — each documented across encyclopedic and community sources.
Your brand: not found in any cited source.
Illustrative mockup — not a real transcript.
Verifiable, reproducible proof
Most AI-visibility reports ask you to take their word for it. We measure real AI citations and hand you evidence you can verify in your own browser — no fabrication, live capture, fully reproducible.
Linked to the live source
Every claim links to the live source the AI cited — a specific Reddit thread or Wikipedia article — not a screenshot or a summary you can't check.
A re-runnable query
Each finding ships with a query you can paste into Perplexity or ChatGPT to see the citation appear live, for yourself.
Captured, not claimed
In one sample deliverable we surfaced three live Reddit threads where the target brands were cited by Perplexity — each with a permalink and a “reproduce it” query.
What we do not promise
The companies pitching 'AI manipulation' are selling a fantasy. AI answer engines aren't a search algorithm you game — they're language models reading the open web. Here's the truth about what we can and cannot do.
We do not
Inject content into ChatGPT or any other model. Their training and retrieval pipelines are not for sale.
We do not
Guarantee that your brand will appear in a specific AI answer. Model providers control their own retrieval logic and grounding.
We do
Build the reliable, neutral, source-verified presence that materially raises the probability of accurate AI mentions — and lets you track whether that probability is going up over time.
Flagship · Packaged
The AI Reputation Stack bundles Wikipedia, Wikidata, multilingual entities, source density and governance into one managed reputation layer — Foundation (€2,490), Authority (€6,900) and Enterprise Reputation OS.
Frequently asked questions
Free AI Visibility Audit
Within 48 hours we show you exactly what ChatGPT, Gemini and Perplexity say about your brand today, plus the highest-leverage gaps.
Keep exploring
Adjacent services that extend this work. Each stands alone — together they compound.
Frequently asked questions
Where to next
AI Visibility Packages
Three fixed-scope programs: €700 / €1,500 / €3,500.
SEO & LLMs Booster Pack
7 wiki platforms, one campaign, −20% on the full pack.
GEO retainer red flags
What to demand before signing any AI-visibility vendor.
ChatGPT wrong about you?
The 6-week correction playbook.
Our machine-readable layer
The crawler infrastructure we run on our own site.
A 3-5 day audit gives you a baseline — and a plan to fix the gaps.