Measuring whether AI assistants recommend you

When a buyer asks ChatGPT for the best home automation company in Mumbai, does it name AHA? When I took over AHA’s website in August, nothing was measuring that, and no dashboard could.

Why you can’t just look it up

Search Console’s API has no AI Overview breakdown. Bing has an AI Performance report in preview, but it covers only Microsoft’s own answers, and I found no API for it. For ChatGPT and Perplexity, the only way to know is to ask them.

What Bing did show, in September, was sobering: only 4 of AHA’s 70 pages had any AI citation at all.

The panel

So I set up a measure with Claude Code: a fixed panel of 12 buyer prompts, from wired versus wireless, to the best company in Mumbai, to adding automation to a finished flat without breaking walls. Each runs on 2 engines, ChatGPT and Perplexity, logged out, in a fresh chat every time. That’s 24 answers a run.

for each of the 12 fixed prompts:
  for each engine in [ChatGPT, Perplexity]:     # logged out, fresh chat
    log: AHA named? · source cited · claim made about AHA · notes
score = answers naming AHA, out of 24

The wording never changes. Edit one prompt and the trend means nothing. “Named” and “cited” are logged separately, because an engine can use your page as a source without saying your name.

The baseline

The first run, on 21 September, named AHA in 5 of 24 answers. Some were strong: Perplexity named AHA first for the best company in Mumbai and cited AHA’s own site. But none of the six explanatory prompts named AHA, and three more answers used AHA’s pages as a source without naming it. The single most-cited source was a directory site, in 5 answers.

The retrofit prompt, adding automation to a finished flat without breaking walls, is AHA’s core work. Neither engine named AHA.

What I changed

• Let AI crawlers in. robots.txt explicitly allows the AI crawlers from OpenAI, Anthropic and Perplexity. In October, all 11 AI crawler user agents tested got a 200 from the live site.
• Bing, properly. Webmaster Tools and the IndexNow key went live on 5 September, but nothing was submitted until 15 September. Then one push sent all 70 live URLs. IndexNow speeds discovery, not ranking, and Google doesn’t use it. The payoff is Bing, which ChatGPT search and Copilot draw on.
• A complete llms.txt, the plain-text index some AI tools read. It now comes from the same data as the sitemap, so it lists 107 of 107 URLs, up from 66, under a short block of verified facts, including what AHA does and doesn’t do.
• One Organization. Twelve hand-copied Organization blocks, and 14 blog publishers using an old brand name, became one node with one @id across the site, with a description, address and service area.
• Content in the HTML. Three homepage sliders were empty until JavaScript built them, so a crawler that doesn’t run JavaScript saw no cards. Their cards are now in the HTML.

A month later

The second run, on 22 October, named AHA in 7 of 24 answers, and named or cited it in 10, up from 8. Perplexity added the Bandra and architects prompts. Bing’s index of the site grew from 84 to 112 pages.

It moves both ways. On the architects prompt, ChatGPT dropped AHA from first to eighth of eight. One answer said AHA installs hardware, which it doesn’t; another quoted an old page title with an out-of-date company age. AI answers lag the site by weeks.

The retrofit prompt still names AHA on neither engine, so the work isn’t finished. The same review found “retrofit” in no title, H1 or H2 on the site. The llms.txt, Organization and slider changes were built after this run, so the next run is the first that can show their effect.

What I’d tell anyone starting

1. Measure before you optimise, with prompts you never edit.
2. Count “named” and “cited” separately. Being a source isn’t being recommended.
3. Make the facts easy to lift: crawlable HTML, one entity, a complete llms.txt.
4. Expect lag, and don’t over-read one run of 24 answers.