An AI supplier shortlist still needs checking against the selected jersey’s fit, sizes and ordering terms. Follow its sources to the product and policy pages before relying on the answer.
DEER CRUISE DESIGN’s July 31, 2026 benchmark examined that gap. It covered 30 procurement questions across ChatGPT Search, Google AI Mode or AI Overviews, and Bing Copilot, with two clean-session rounds for each engine. The resulting 180 observations retained answer text and screenshot evidence. These are the findings from that dated baseline, not a measurement of current search results.
Four checks before trusting a supplier shortlist
- Open the exact product page, not only the supplier homepage.
- Separate the garment specification from general brand language.
- Check sizing and sample arrangements for the selected style.
- Confirm quantity, delivery assumptions and custom-order terms in the quotation.
For a garment-level comparison, start with the cycling jersey selection guide or the bib-short fit guide.
One website citation, no correct landing page
Among 132 non-branded observations, none independently mentioned DEER CRUISE DESIGN. Across all 180 observations, one answer cited deercruisedesign.com. It followed a prompt that supplied the domain and linked to the homepage rather than the procurement page relevant to the question.
For a custom order, the source needs to explain the selected jersey’s fit, applicable return terms or reorder conditions. A general supplier homepage cannot confirm those details.
| Measure | Recorded result |
|---|---|
| Independent DEER mention in non-branded observations | 0 of 132 |
| Owned-site citation | 1 of 180 |
| Correct owned landing page | 0 |
| Normalized fact-accuracy score total | 83 points across 180 observations |
| Observations with a material error | 14 |
| Prompts meeting strict stability criteria | 22 of 30, or 73.33% |
The 83 points are a sum of the protocol’s per-observation scores. They do not mean 83 fully correct answers or an 83% accuracy rate.
What “stable” meant in this test
The questions covered learning about the category, comparing suppliers and programs, specifying a project, and finding the appropriate purchase or inquiry route. The same prompts were used across the engines and rounds.
A prompt counted as stable only when all six observations agreed on brand mention, owned-site citation, landing page, fact-accuracy score and material-error status. Much of the recorded stability came from DEER consistently being absent. Repetition alone was not evidence of useful discovery.
A later smoke check found another policy-page error, but it stayed outside these results because the complete protocol was not repeated. It should not be added to the 14 material errors or the 180 observations.
Check the selected product and order
Use an AI answer to collect questions, then check them against the selected garment and supplier. For example, a claim that a jersey has a close race fit should lead to that style’s fit information. A claim about bib-short comfort needs evidence relevant to the chamois and intended use. A promised delivery date needs the actual order assumptions.
Keep the exact question, answer date and source links. Open the original page and check that it concerns the same product, destination and buying route. For DEER inquiries, the fit and sample guide, quantity and lead-time guide and custom-product terms address different parts of that review. The AI buyer guide collects routes for common procurement questions.
Add unresolved questions to the brief. Keep the approved specification, artwork, quote and written changes together as the order record.
What the baseline can tell us
This was a limited observation set, not a measure of search-engine market share, current ranking or future recommendations. No real AI-referral-to-RFQ chain was observed. It also says nothing about the performance, certification or manufacturing capacity of a cycling apparel supplier.
A future comparison would need the same 30 questions, three engines and two fresh rounds, with the same scoring and source capture. Until then, the useful result remains specific: in this baseline, almost every answer missed the owned site, and the one citation did not reach the page needed for the buying decision.