Every run behind this site

Does an assistant name local businesses to their customers?

27 August 2026 – 30 August 2026 · raw_observations.jsonl

The first question worth asking: when an ordinary person asks an assistant for a local service, does it answer with names, or with advice? We asked across trades and metros, one question per fresh conversation, full answer text stored verbatim.

It answers with names, every time, in a short business list. This run is the calibration set every later comparison is measured against, and it is where the pool figures on the category pages come from.

The same question, ten minutes apart

30 August 2026 – 31 August 2026 · service_intent_raw.jsonl

Five kinds of question a business owner might put to an assistant when looking for help, worded for three trades – the four things such an owner can buy, plus the vague version of the same need. Asked repeatedly, on one model tier, so repeats could be compared.

Every reading returned a shortlist; not one deflected into generic advice. But two readings of one identical question shared an average of 15% of their named companies, and the sharpest pair shared none at all. Being named once is a reading, not a position.

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A fifth pass on the same five questions

31 August 2026 · stability_raw.jsonl

A further repeat of the service-intent set, recorded to a separate file so the earlier readings could not be quietly extended. Same assistant, same account, same tier discipline.

Pooled with the run above, this is what the stability figures rest on. Both files record the same assistant and account, so they are compared with each other and never with the consumer-question readings.

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What an assistant tells a business owner looking for help

29 August 2026 · raw_buyer_probe.jsonl

Ten questions in the owner's own voice – my dental practice does not show up when people ask ChatGPT, who can help my roofing company, who does AI visibility audits for small local businesses.

Nine of the ten produced a named shortlist of vendors, with prices and a best-for column. These questions get a buying answer, not advice – which is the single most commercially useful thing we have measured.

The same ten questions, framed in Austin

30 August 2026 · raw_buyer_us.jsonl

The probe above inherited its operator's location. We ran it again stating a US metro, to find out whether the answer set is national or local.

Almost nothing carried over. The names came back Austin-specific, down to firms with the metro in their brand line.

And again, in Miami

30 August 2026 · raw_buyer_miami.jsonl

A third locale, to test whether the Austin result was localization or noise.

Roughly 3% of names overlapped between Austin and Miami, and not one company appeared in all three runs. Inside a single metro the answers concentrate hard on one or two firms. This is a per-metro market as an assistant sees it.

Ten trades, three cities, repeated readings

31 August 2026 – 2 September 2026 · vertical_raw.jsonl

Ten verticals, a consumer question each, in Austin, Miami and Columbus, asked more than once so stability could be computed per trade rather than site-wide.

Complete. Every reading used web search, so a page can reach these answers at all. The pools differ by more than six times between trades, which is why the remaining 36 categories are worth running. And the sharpest result: asked in three metros, not one of 19 comparable city pairs shared a single business.

Thirty-six more trades, one city, repeated readings

1 September 2026 – 13 September 2026 · vertical_wave2_raw.jsonl

The other thirty-six categories on this site, each asked the consumer question its own customers would ask, in Austin, more than once. One metro rather than three: the previous run had already shown that the answer is rebuilt per city, so a second city would have re-measured a settled question instead of covering new trades.

Stopped short in September when the account began returning challenges rather than answers, then topped up on 13 September: the eleven trades that had been left with two or three readings now carry eight or nine each, which is what a stability figure needs. What came back: thirty-six trades asked over one hundred and eighty readings; in thirty-four of the thirty-six trades the answers named businesses, and every single reading used web search – so a page can reach these answers in all forty-six categories we sell to, not just the ten measured before. The pool an assistant draws on ran from two businesses to seventeen, and of the five hundred and sixty-one pairs of trades, five hundred and fifty-nine shared no business at all; the two that did were single clinics that genuinely sell both services. Because the rate limits spread these readings out, they also answered something the earlier run could not: the same stability holds across three hundred and four hours, where the first wave could only see forty-nine.

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