Reverse domain lookup for AI citations (how it works)
The mechanic under the hood: an inverted index of which domains the AIs cite for which questions, queried backwards — by domain instead of by question.
A reverse domain lookup for AI citations takes a domain as input and returns every buyer query that AI assistants — ChatGPT, Gemini and Grok — already cite or mention it on. It works on an inverted index: a background system continuously asks the three models a broad range of real questions, extracts the domains each answer cites, and stores question → domain links. Normally you query that index in the forward direction (ask a question, see who is named). The reverse lookup flips it — you query by domain and read out the list of questions where that domain appears, along with the intent, which of the three models cited it, and the competitors named alongside. It is the same pattern as an Ahrefs Site Explorer backlink lookup, applied to the answer layer instead of the link graph.
What is a reverse lookup, conceptually?
A forward lookup goes question → answer: you ask “what are the best project management tools?” and the model names a handful of domains. A reverse lookup goes the other way: you start from yourdomain.com and ask “which questions cause the models to name this domain?” To answer that, you can’t guess — you need a record of many answers to look back through. That record is the index, and reading it by domain is the reverse lookup.
How is the index built?
The mechanic is deliberately boring and repeatable — that is what makes it trustworthy. A background worker runs on a continuous cycle:
- Mine queries. Collect a broad, representative set of real buyer questions — discovery questions, “best tool” questions, comparison and problem questions — across topics.
- Ask all three models. Run each question through ChatGPT, Gemini and Grok. The three are kept separate because they routinely cite different sources for the same question.
- Extract domains. Parse each answer for the domains it cites (linked sources) and mentions (named brands), normalising URLs to a clean root domain.
- Write the links. Store each question → domain pair, deduped on a normalised query key so the same question isn’t double-counted.
- Refresh. Re-run on a cadence, because AI answers change — see the pillar for why a snapshot isn’t enough.
The result is an inverted index — the same data structure a search engine uses to map terms to documents — except here it maps questions to domains across three answer engines.
What does the reverse lookup return?
When you enter a domain, the lookup reads the index backwards and returns, for each matching query:
- The query — the literal question asked.
- Intent — informational, commercial or transactional.
- Model coverage — which of ChatGPT, Gemini and Grok named the domain.
- Citation vs mention — whether the domain was linked as a source or named in prose (see mention vs citation in AI).
- Competitors — the other domains in the same answer.
That combined output is your AI citation footprint.
Why not just ask ChatGPT “what do you cite me on?”
Because a model can’t reliably introspect its own future answers. Ask it directly and it will improvise a plausible-sounding list that may not match what it actually returns to real users. The only dependable method is to observe real answers to real questions and record them — which is what the index does. The model is the thing being measured, not the measurer.
How fresh is the data?
Freshness depends on cadence. A shared global index gives broad coverage that refreshes periodically; a monitored project narrows in on one domain’s relevant queries and refreshes them far more often. The reverse lookup always reflects the most recent recorded answers — and because the index only grows, you also build a history of how a domain’s footprint changes over time.
Try the reverse lookup yourself
The free Domain Check is a reverse domain lookup you can run in seconds: enter a domain and read the live query list across all three models. For the bigger picture and the “Site Explorer for AI” framing, start at the Reverse AI Search pillar or read the Site Explorer for AI analogy.