50 UK fashion brands. One scorecard. We tested how well AI models — ChatGPT, Perplexity, and Claude — can find, understand, and recommend these brands across buyer-relevant prompts.
Each brand was tested against a battery of fashion buyer prompts — style queries, occasion styling, quality comparisons, fit guidance, material sourcing — across ChatGPT (GPT-4o), Perplexity (Sonar Large), and Claude (Sonnet 4) in June 2026. A brand "wins" a prompt when it appears in a recommendation, citation, or comparison. Score = percentage of prompts won, out of 100. Brands also scored on their structured data completeness: FAQPage schema, product JSON-LD, breadcrumbs, and brand entity markup.
Search, sort, or scroll. Click column headers to sort by score or brand name.
| # | Brand | Score ↕ | Grade | Top Gap | vs Category Avg |
|---|---|---|---|---|---|
| 1 | Stella McCartney | 67 | Grade C | No FAQPage schema | +17.4 pts |
| 2 | Reiss | 65 | Grade C | Zero JSON-LD product markup | +15.4 pts |
| 3 | Dr Martens | 64 | Grade C | No FAQPage schema | +14.4 pts |
| 4 | Me+Em | 63 | Grade C | No breadcrumbs markup | +13.4 pts |
| 5 | Hush | 61 | Grade D | Missing product JSON-LD | +11.4 pts |
| 6 | Boden | 61 | Grade D | No FAQPage schema | +11.4 pts |
| 7 | Finisterre | 58 | Grade D | Slim product descriptions | +8.4 pts |
| 8 | Sweaty Betty | 58 | Grade D | No brand entity markup | +8.4 pts |
| 9 | Veja UK | 58 | Grade D | No FAQPage schema | +8.4 pts |
| 10 | Sunspel | 57 | Grade D | No product structured data | +7.4 pts |
| 11 | John Smedley | 57 | Grade D | No breadcrumbs markup | +7.4 pts |
| 12 | Allbirds UK | 56 | Grade D | No FAQPage schema | +6.4 pts |
| 13 | Toast | 55 | Grade D | No JSON-LD product markup | +5.4 pts |
| 14 | Asket UK | 55 | Grade D | No FAQPage schema | +5.4 pts |
| 15 | AllSaints | 54 | Grade D | No brand entity markup | +4.4 pts |
| 16 | Nobody's Child | 53 | Grade D | Zero structured data | +3.4 pts |
| 17 | Drake's | 53 | Grade D | No FAQPage schema | +3.4 pts |
| 18 | Folk | 52 | Grade D | No product JSON-LD | +2.4 pts |
| 19 | Finisterre (2) | 52 | Grade D | Slim product descriptions | +2.4 pts |
| 20 | Vivobarefoot | 51 | Grade D | No breadcrumbs markup | +1.4 pts |
| 21 | Percival | 51 | Grade D | No FAQPage schema | +1.4 pts |
| 22 | Whistles | 51 | Grade D | No JSON-LD product markup | +1.4 pts |
| 23 | L'Estrange | 50 | Grade D | No brand entity markup | +0.4 pts |
| 24 | Cefinn | 49 | Grade D | Zero structured data | -0.6 pts |
| 25 | Mother of Pearl | 49 | Grade D | No FAQPage schema | -0.6 pts |
| 26 | Universal Works | 49 | Grade D | No product structured data | -0.6 pts |
| 27 | Wax London | 48 | Grade D | No breadcrumbs markup | -1.6 pts |
| 28 | Ninety Percent | 48 | Grade D | No JSON-LD product markup | -1.6 pts |
| 29 | Oliver Spencer | 48 | Grade D | No FAQPage schema | -1.6 pts |
| 30 | Lucy & Yak | 47 | Grade F | Zero structured data | -2.6 pts |
| 31 | Rixo | 47 | Grade F | No product JSON-LD | -2.6 pts |
| 32 | Realisation Par | 46 | Grade F | No FAQPage schema | -3.6 pts |
| 33 | Sandqvist UK | 46 | Grade F | No breadcrumbs markup | -3.6 pts |
| 34 | Seasalt Cornwall | 46 | Grade F | No brand entity markup | -3.6 pts |
| 35 | YMC | 45 | Grade F | Zero structured data | -4.6 pts |
| 36 | Sezane UK | 44 | Grade F | No FAQPage schema | -5.6 pts |
| 37 | Ghost London | 44 | Grade F | No JSON-LD product markup | -5.6 pts |
| 38 | Aspiga | 44 | Grade F | No product structured data | -5.6 pts |
| 39 | With Nothing Underneath | 42 | Grade F | No breadcrumbs markup | -7.6 pts |
| 40 | Lemaire UK | 42 | Grade F | Zero structured data | -7.6 pts |
| 41 | Rouje UK | 43 | Grade F | No brand entity markup | -6.6 pts |
| 42 | Bleusalt | 43 | Grade F | No FAQPage schema | -6.6 pts |
| 43 | Albam | 43 | Grade F | No JSON-LD product markup | -6.6 pts |
| 44 | Lavender Hill Clothing | 41 | Grade F | Zero structured data | -8.6 pts |
| 45 | Olivia von Halle | 41 | Grade F | No product structured data | -8.6 pts |
| 46 | Beulah London | 40 | Grade F | No FAQPage schema | -9.6 pts |
| 47 | Damson Madder | 39 | Grade F | No breadcrumbs markup | -10.6 pts |
| 48 | Community Clothing | 38 | Grade F | Zero structured data | -11.6 pts |
| 49 | Tropicfeel UK | 37 | Grade F | No brand entity markup | -12.6 pts |
| 50 | Riley Studio | 36 | Grade F | No FAQPage schema | -13.6 pts |
We audited structured data markup across all 50 brands. The results are not flattering.
FAQPage schema are invisible to fashion-related queries like "best linen shirts for summer" or "where to buy sustainable denim".The top performers aren't perfect — but they're doing 2-3 things the rest aren't.
Me+Em's category pages are deeper than any other brand in this audit. Each category has editorial-style intros, material guides, occasion-based filters, and styling advice. That content depth gives AI models significantly more to cite when answering fashion queries. Missing breadcrumbs markup, but content strategy is doing the heavy lifting.
Boden wins on category breadth and brand entity clarity. A clearly defined family fashion positioning — with distinct sub-brands and clear occasion/style taxonomy — gives AI models a strong signal for a wide range of fashion queries. Strong FAQ content on fit and sizing (even without schema) gives AI enough to work with.
Wins on activewear category authority — a strong niche signal that AI models associate with quality activewear. The brand's product descriptions include performance fabric detail, activity-specific fit guidance, and sizing by activity type. Content quality in the performance-wear sub-niche pushes them above the category average.
Drake's wins on MADE IN ENGLAND provenance and heritage craft story. Est. 1977, London-made, small-batch production — these are exactly the kind of E-E-A-T signals AI models use to assess brand quality and authenticity. The brand story translates into machine-readable trust signals even without heavy structured data.
Finisterre's sustainability story is well-developed and well-indexed — technical fabric provenance, ethical sourcing credentials, and ocean-wear positioning. The brand's content on materials and environmental commitments gives AI models the kind of detailed sourcing information they use to assess fashion brand quality and mission alignment.
These brands are leaving AI visibility entirely on the table. Here's exactly what's broken.
No FAQPage schema. No product JSON-LD. No brand entity markup. No breadcrumbs. The brand has good sustainability credentials but none of them are in a format AI models can read. A clean t-shirt with no structured data is invisible to "best sustainable clothing brands" queries.
Has a strong sustainability story but the content is on-page only — no structured data. "Sustainable travel clothing" is a growing AI query class but Tropicfeel has no FAQPage, no product JSON-LD, and no brand entity markup. The brand exists in the AI blindspot.
Product descriptions are thin and lack the fabric/make/origin detail that AI models use to assess quality. On top of that, there's zero structured data. Brands in this bracket need both better copy AND better markup to start moving the needle on AI visibility.
Small, independent, UK-made. The brand's story is compelling but it's not reflected in structured data. No FAQPage, no product JSON-LD, no brand entity markup. The AI models have nothing to latch onto. Strong brand story + zero schema = complete invisibility to AI search.
A heritage brand with a good reputation but zero structured data. No FAQPage schema, no breadcrumbs, no brand entity markup. The brand's awards and press coverage aren't connected to any machine-readable format. Heritage alone doesn't move AI visibility without structured data to anchor it.
The same 3 failures appear across 94% of the 50 brands we audited.
Almost every fashion brand we audited has a style guide, fit guide, or FAQ page — but none of them have FAQPage schema markup. AI models struggle to parse on-page FAQ content without the structured markup to tell them what they're reading.
Product JSON-LD tells AI models what a product is — name, price, description, rating, availability, material, color. Without it, your products are unstructured text in a sea of unstructured text. Brands like Amazon and Asos have had product schema for a decade.
Product JSON-LD to every PDP: name, image, description, SKU, brand, offers (price, availability), aggregateRating. Estimated lift: +6-12 pts.AI models need a "brand entity" to attach attributes to. Without Organization or Brand schema, there's no anchor for the brand's awards, sustainability credentials, or heritage story. The brand exists in the training data as text — but not as a structured entity with attributes.
Brands with deep category page content (material guides, fit advice, occasion styling) score measurably higher than brands with 2-line product descriptions. The AI models use your page content as the source text for answers. More depth = more to cite.
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