Answer engine optimization

How to get cited by ChatGPT using Cursor

ChatGPT does not recall your brand from memory. It retrieves a handful of pages and writes its answer from them, so getting cited means getting onto those pages, in a form it can lift. With Cursor, the workflow is: find the buyer questions where ChatGPT names a competitor instead of you, look at which URLs it keeps citing for those questions, and publish a page that out-answers them - leading with the answer, structured as a table or list, and honest about where competitors genuinely win. Then re-measure a few weeks later, because publishing without re-measuring is guessing.

How ChatGPT actually decides who to name

When a question implies recency or a recommendation, ChatGPT searches the web and writes its answer from the handful of pages it retrieves. It is not recalling your brand from training data - it is reading a few pages, right then, and summarising them. So the question is not 'how do I get into the model', it is 'how do I get onto the pages it pulls'.

What moves the number on ChatGPT

1. 1Get named on the third-party pages it retrieves - the roundups, comparisons and 'best X for Y' listicles that already surface for your query. Being in someone else's list beats a page on your own domain, because the model treats an independent source as more trustworthy than a vendor claiming to be the best.
2. 2Answer the exact question in the first 100 words of your own page. The model lifts the passage that answers the question; if yours starts with a brand story, it lifts a competitor's.
3. 3Publish a comparison table that names your competitors honestly. Tables survive chunking intact and get quoted more than prose, and a page that surveys the field is the kind of source a model cites when asked to survey the field.

Doing it with Cursor

An AI code editor. Useful here because it can hold your whole site in context, which makes it the right tool for fixing the structural reasons AI cannot read you.

1. 1Point Cursor at your site's repo and ask it to find every page that answers a buyer question but buries the answer below the fold. That inversion - answer first, story second - is the single highest-leverage edit for AI citation, and it is a code change, not a marketing one.
2. 2Have it audit crawlability the way an AI engine sees you: is content server-rendered or does it need JavaScript, does robots.txt block the AI crawlers you actually want (GPTBot, PerplexityBot, Google-Extended), do pages carry usable structured data.
3. 3Generate the comparison table as a real HTML table, not an image or a flex-box grid pretending to be one. Models parse tables; they cannot parse a screenshot of a table.
4. 4Wire the Fulcru MCP server into Cursor so the gap list and the draft come from measured data rather than from the model's guess about your market.

What this will not do

The part everyone skips

Re-measuring. A published page that you never check is a belief, not a result. Run the same question set again a few weeks later and compare: were you named in 2 of 10 answers before, and 5 of 10 after? That number is the only thing that tells you the work landed - and when it does not move, that is information too. It usually means the answer set is dominated by a source you have not gotten onto yet.