Field notes
03
We asked 11 AI models for Airtable agencies, then for Airtable implementation agencies, 220 times in all. The extra word changed nothing: the same top four in the same order, and about one pick in five still went to software like Softr and Stacker. The brand name in the question and the model answering it decide the answer. The wording barely matters.
2 Oct 2026 · 5 min read
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Whether rewording a buyer question to be more specific makes AI models give a more specific answer.
The test
We ran the same panel of 11 models on two versions of the same buyer question, 10 times each, answering from training with no web search:
The casual study was already a bit off. Alongside real agencies, the models recommended Softr and Stacker, which are no-code app builders that run on top of Airtable, not agencies. Adding "implementation" should have made it clear we wanted a firm to do the work for us, not a tool. So we expected that version to cut the software out.
Finding 1
The top four came back identical, in the same order, and with almost the same scores.
Further down it's the same brands in a slightly different order. 9 of the top 10 and 16 of the top 20 are shared between the two studies. GAP Consulting was ranked first in 29 answers to the casual question and 32 to the precise one.
Finding 2
We counted every pick that isn't an agency: software products, integration platforms, freelance marketplaces, and stand-ins like "Local freelance consultants". The share barely moved.
109 of 540 picks for the casual question, 101 of 540 for the precise one.
Softr did drop, from 19 picks to 13. But Stacker went up from 18 to 21 and BaseDash from 3 to 7, so the software share stayed about the same. A tool was ranked first in 11 answers to the casual question and 9 to the precise one.
The precise question also produced more generic stand-ins, like "Airtable Experts (by Airtable)", "Certified Freelancers" and "Local freelance consultants". These are categories of provider, not brands.
Finding 3
Look at what the non-agencies are. They aren't random software. Nearly all of them are part of the Airtable ecosystem:
These are front ends, extensions and integrations built for Airtable. To a model, "Airtable" is a much stronger signal than "agency" or "implementation", and these brands sit right next to it. They rank lower (on average 3.2 to 3.4 out of 5), but they are pulled in all the same.
The models often know they're off-brief, and they list them anyway:
"It is fundamentally a no-code platform rather than an Airtable implementation agency. Because the query seeks specialized agencies, Softr is the least direct fit."
Others make the tool fit the question. Asked for implementation agencies, mistral-medium-3.5 described Stacker, a software product, as a company that "offers Airtable implementation services", and ranked it third.
Finding 4
Split by model, the share of non-agency picks is almost the same in both studies. A few models produce nearly all of it.
The long tail works the same way. The casual question produced 153 different brands, 101 of them named only once; the precise one produced 142, with 91 named once. Most of those one-off names come from claude-haiku-4.5 and qwen3.8-max, and 38% to 62% of their picks are brands no other answer mentions.
What it means
Adding "implementation" to the question didn't make the answer more precise. What a model recommends here is driven mostly by the brand name in the question and by which model is answering. The rest of the phrasing makes little difference.
01
Don't expect prompt wording to clean up a category. If the category is named after a platform, the platform's ecosystem will show up in the answer, however precisely the question is worded.
02
For an ecosystem brand, adjacency is visibility. Tools built around Airtable take about one pick in seven in a category they don't belong to, just by being close to the word "Airtable".
03
Compare models, not phrasings. Rewording the question changed the non-agency share by a point and a half. Switching model changes it by up to 72 points.
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