AI search visibility (AEO / GEO)
The industry calls this AEO (Answer Engine Optimization),GEO (Generative Engine Optimization) or LLM SEO. Different names, one job: when AI answers your buyers, your brand is in the answer.
Your buyers ask ChatGPT which brands to shortlist. They ask Perplexity to compare vendors. Google now answers directly with AI Overviews. In every one of those answers, you are either named or you are invisible — and most businesses have never once checked which it is.
What we measure, per engine
Engines are not interchangeable. Each one retrieves from different sources and behaves differently, so we measure and optimise them separately:
| Engine | What we measure | What we optimise |
|---|---|---|
| ChatGPT | Citations in web-search answers (Bing-grounded — your Bing index matters) | Bing indexing, citable source pages, entity clarity |
| Perplexity | Cited domains per answer, position in the citation list | The third-party and owned pages Perplexity repeatedly pulls from |
| Google AI Overviews / AI Mode | Inclusion in the AI answer, linked sources | Classic SEO foundations plus answer-shaped content |
| Gemini | Brand mentions and recommendations for buyer questions | Google surface signals, structured entity data |
| Claude | Citations in web-search answers | Crawlable, factual, well-structured source pages |
| Copilot | Citations in answers (Bing-grounded) | Bing indexing and the same source set as ChatGPT |
Our method, honestly
- Sampling, not screenshots. A single AI answer is one sample from a distribution. We ask each question repeatedly and report share-of-voice per engine across runs.
- Monitoring is not fixing. Each package states plainly who does the implementation work. Tracking a number does not move it.
- No vendor's guidance is treated as complete truth.Google published AI-search guidance in May 2026; we treat it as one engine's stated policy and test it against what we measure, not as a universal rulebook.
- No overclaiming. Schema markup helps entity clarity and correlates with citation — causation is not established, and we say so. FAQ rich results in Google were deprecated in 2026; we do not promise rich snippets.
Offers
AI Visibility Audit — one-time
Your buyers' questions run across the engines above, repeated sampling, a report showing where you are cited, who is cited instead, which domains each engine pulls from, and a prioritised fix list. You implement, or we do in a follow-on project.
Implementation — project
We do the fixes from the audit: source-page content, entity and schema work, Bing/Google indexing foundations, and the third-party citations each engine responds to. Re-measured against the audit baseline.
Monitoring & optimisation — monthly
Weekly share-of-voice logging on your question set per engine, monthly reporting, and ongoing optimisation. Tiers by number of prompts tracked and engines covered. Month-to-month, no lock-in.
The founder's audit toolkit
Audits run on my own toolkit — Perplexity Sonar and ChatGPT web search wired into a local Streamlit app — plus manual verification runs. It is run by a human for every audit; there is no self-serve endpoint. Screenshots of the tool will appear here shortly.
Common questions
What is AEO / AI search visibility?
Answer Engine Optimization (AEO) — also called Generative Engine Optimization (GEO) or LLM SEO — is the work of getting your brand cited and recommended inside AI assistants like ChatGPT, Perplexity, Gemini and Claude, the same way SEO targets Google's results page. We measure your current visibility per engine and do the implementation work to improve it.
How do you measure AI visibility?
We ask a fixed set of your buyers' questions on each engine, repeatedly over time, and log whether you are cited and who is cited instead. One answer is one sample; we report share-of-voice across repeated runs per engine, never a single screenshot.
Can you guarantee my brand will be cited by ChatGPT?
No, and nobody honestly can. Each engine retrieves differently and results vary run to run. What we guarantee is a documented method, per-engine measurement before and after the work, and implementation targeted at the sources each engine actually pulls from.
Does schema markup or llms.txt improve AI rankings?
Schema markup helps machines understand your entities and correlates with citation, but causation is not established. llms.txt makes your site easier for AI agents to read; it is not a ranking lever and we do not sell it as one.
Start with the free check
Give us your website and the five questions your buyers ask AI. We run them across ChatGPT, Perplexity, Gemini and Claude and send you where you stand — free, within two business days.