Grounding Fidelity
Most SEO tools guess how AI engines see your brand. Cite AI queries the real engines with their live web search on, capturing exactly what they search, read, and cite.
Scraping vs. Grounding
Traditional AEO tools scrape standard search results (SERPs) and use a heuristic script to estimate which pages an LLM might include in its response.
Cite AI is built on Grounding Fidelity. We query each engine directly with its live web search on. When we run a prompt, the engine searches, reads the source pages, and records which ones it cited — and we capture that record directly, giving you the real, unfiltered data.
- 1Ask every engine, liveWe run your prompts through ChatGPT, Claude, Gemini, and Perplexity with their real web search switched on — exactly how your customers ask them.
- 2See what they actually readWe capture the real pages each model pulled up while forming its answer — not a guess at what it might have used.
- 3Pinpoint every citationWe find the exact spot in each answer where your brand was cited — or a competitor was — so every metric ties back to a real response.
Inside the LLM Search Loop
What actually happens when an AI engine answers a buyer's question — and where Cite AI captures the proof of whether you were cited.
1. The engine runs a live search
When we send a prompt like "best business checking accounts", the engine recognizes it needs current information and runs a real web search — the same live lookup it does for your customers, rather than answering from memory.
2. It reads the pages it found
The engine pulls up a set of real pages — often 20 or more — and reads their content to shape its answer. Cite AI records every one of these as a "Searched" page, so you can see which sources were in the running, including your own.
3. It writes the answer and cites its sources
As the engine writes its answer, it links specific sentences back to the pages it used. Cite AI ties each of those citations to the exact passage in the answer — so you can see precisely where you were named, where a competitor was, and which of your pages got read but skipped.
National and local prompts
AI answers change depending on where the person asking is standing. Every prompt can run nationally, or carry a location so you see exactly what a customer in that market gets.
"best business checking accounts"
No location attached, so you see the answer a nationwide audience gets — the right lens for brands competing across a whole country.
"best med spa"
Tag a prompt with a location and Cite AI passes it to each engine's live search, so the answer reflects what a searcher in that city actually sees — including the nearby competitors the AI names.
For local businesses, that distinction is everything: a med spa, law firm, or dealership only cares whether it shows up when someone nearby asks — a national score hides that. Tag a prompt for each market you serve and every run asks from there. In Analytics, switch between one market, all of them, or the national baseline — so you know exactly where you win and where a local rival owns the answer.
How precisely each engine localizes
National projects run all six engines. Local projects run the four that can resolve a place — here is exactly what each one can tell you about a specific market, and which lever moves it.
| Engine | Local precision | What we can tell you locally | Lever |
|---|---|---|---|
| ChatGPT | Point-levelMeasured at every grid point | Which businesses ChatGPT names, and which sites it cites, for a searcher standing at a specific set of coordinates. Run at every point of a grid, nine or twenty-five readings across one metro rather than one. | Website content and structure for the citations; the Google Business Profile for the businesses it names off the map. |
| Google AI Overviews | City-level | The AI answer at the top of Google, captured once from within the market you name — the same answer a searcher standing there gets. (Google’s map pack is read separately at every grid point; that is where your ranked position comes from, and it counts under the map surface rather than under this engine.) | Website content and structure; the local pack alongside it is driven by the Google Business Profile. |
| Google AI Mode | City-level | Google’s conversational search, run against a named location rather than a generic national query. | Website content and structure. |
| Google Gemini | Point-levelMeasured at every grid point | Two surfaces, measured differently. The businesses Gemini surfaces from Google’s listings are read at every point of the grid — its Maps grounding takes coordinates, and in our nine-point test across one metro it named a substantially different set at each. The websites it cites are read once for the market: we have not measured whether that layer varies inside a metro, so we do not grid it. | Google Business Profile, and reviews specifically. Holding Google rank fixed at 1-10, Gemini named 29% of businesses under 50 reviews and 75% of those past 200, and the pattern held in crowded and thin markets alike. It is the one lever we have measured moving an engine. |
| Claude | Not run locally | Nothing local. Claude’s API returns cited links and no place data — the map results you see in the Claude app come from Google Places, which the API does not expose. We drop it from local projects rather than bill you for a reading that cannot see a place. It runs in full on national projects. | Website content and structure, at national scope. |
| Perplexity | Not run locally | Nothing local. Perplexity has no map surface, and its location signal did not separate one metro from another in our testing — so we drop it from local projects rather than present national results as local ones. It runs in full on national projects. | Website content and structure, at national scope. |
A local project measures four of the six engines. Claude's API returns no place data, and in our own testing Perplexity's answers didn't differ between one metro and the next — so neither runs on local projects. Both run in full on national ones. Where an answer draws on Google's business listings rather than the open web, the lever is your client's Google Business Profile, not their website.
Why Grounding Analytics Matters
Searched vs. Cited Gaps
Identify pages that the model actually retrieved and read but chose not to cite. These are "Crawled, Not Cited" opportunities where tweaking your content structure can trigger inclusion.
Snippet-Level Proof
Instead of just counting mentions, show stakeholders or clients the exact paragraphs and quotes the AI pulled from your pages to back up its recommendations.
Statistical Integrity
Answer engines are highly volatile, frequently swapping citations between runs. Measuring actual API execution traces lets you separate temporary model noise from real organic trends.
See where you stand in AI answers — and what to do about it.
Cite AI pairs verified citation metrics with content briefs you can hand to your dev team or action yourself — so visibility data turns into pages that get cited.