Field study / July 2026

Three form backends disappeared from every open AI shortlist.

We tested five active products across Gemini and Perplexity. The central pattern was not misunderstanding. It was failure to enter the shortlist before the product was named.

Usable answers30/30No blocked runs in the final matrix
Invisible in all open prompts3/5Form.taxi, Basin, Formspark
Understood when named4/5Recognized by both surfaces
01

What we tested

Each product received the same three prompt types on the available consumer web versions of Gemini and Perplexity. We preserved the answer and checked whether the target and direct competitors appeared.

PromptBuyer questionSignal
P1Open category discovery for a static-site contact formCan the product enter an unprompted shortlist?
P2Feature-led selection: spam protection, setup, free tierCan requirements retrieve the product?
P3Named comparison against four direct alternativesCan the model understand and position it?
02

Open discovery results

The chart shows appearances across the four open P1/P2 answers. A zero does not mean the product is unknown. It means the product did not enter these small buyer shortlists before being named.

The gap appeared before comparison.

Form.taxi, Basin, and Formspark were absent from all four open answers even as Formspree, Web3Forms, FormBold, FormSubmit, Netlify Forms, and other alternatives recurred.

03

Named understanding was different

When the comparison prompt explicitly named the products, Gemini and Perplexity both recognized Form.taxi, Basin, Formspark, and FormBackend. FormBold was recognized by Gemini but omitted by Perplexity even in the named comparison.

ProductOpen answersNamed comparisonObserved pattern
Form.taxi0 / 42 / 2Understood, but not retrieved by default
Basin0 / 42 / 2Understood, but not retrieved by default
Formspark0 / 42 / 2Understood, but not retrieved by default
FormBackend3 / 42 / 2Strongest open discovery in this sample
FormBold1 / 41 / 2Uneven discovery and named recall
04

What this does and does not show

  • It shows a repeated omission pattern in one dated, controlled sample.
  • It does not prove a stable AI ranking or lost revenue.
  • It does not measure product quality.
  • It does show why named-product tests alone can create false confidence.
  • It provides a concrete starting point for reviewing category pages, use-case facts, comparisons, FAQs, and public evidence.
The practical question is not “Does the model know us?” It is “Does the model retrieve us when a buyer has not supplied our name?”
05

Study limits

AI answers vary by date, location, model version, interface state, and prompt wording. This study used two accessible consumer web surfaces and three prompts per product. It should be treated as a field observation, not a population estimate.

No company named here commissioned the study. Product names are used only to report observed public AI outputs. AgentRank has not claimed that any omission was caused by a specific website defect.

Run the same check for your product.

The USD 99 Audit Lite covers five buyer prompts, up to four available AI surfaces, competitor evidence, and a prioritized repair roadmap.

Request an audit