For apparel brands · 50–5,000 SKUs · £1M–£50M revenue
Apparel catalogs don’t lose to trends. They lose to a missing size field.
AI shopping agents filter apparel on size, colour, material and gender. When those fields live in your product descriptions instead of structured metafields, the agent skips your store. Eight apparel-specific mistakes below — each one observable in a real Shopify catalog, each one scored on every Flintmere scan.
Apparel benchmark
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awaiting first scan
Apparel scores appear here as soon as the first stores land in the dataset. Run yours to seed it.
Run the free scan →Eight apparel-specific mistakes
Each one is concretely observable. Each one is scored on every scan.
Size options written as free-text, not structured data
Your variants say 'Small' on some products, 'S' on others, 'small' on the ones imported from a 2022 spreadsheet. An agent answering 'show me medium dresses' can't tell they're the same size, so it skips yours for a competitor whose sizes normalise.
Flintmere: Flintmere checks each variant for the structured size metafield and flags the free-text ones. We show you the exact product IDs to fix and the canonical size token for each.
Check · Structured attributes
Colour names only a human could parse
Midnight Oasis. Forest Whisper. Dusty Rose Quartz. Beautiful copy — every apparel merchandiser writes it — but agents asked for 'red dress' don't know Dusty Rose Quartz is red. Your catalog scores zero on colour-filterable queries.
Flintmere: We scan every variant for a structured colour field with a normalised token (red, blue, green …) alongside your merchandised name. Keep the romance copy; add the token agents can match.
Check · Structured attributes
Material composition buried in description prose
Your description says 'buttery-soft signature blend'. Shoppers love it, agents don't parse it. The next merchant writes '95% cotton, 5% elastane' in a structured field and wins every composition-filtered query.
Flintmere: Flintmere flags products missing a composition metafield and offers a regex that extracts composition strings from existing descriptions — so you can bulk-populate the field without re-typing every product.
Check · Structured attributes
Size chart delivered as a 1,200×800 PNG
Your size chart is a designed image. Stunning for shoppers, invisible to agents. Returns rate stays high because agents can't answer 'will a UK 12 fit me?' and shoppers order one size up 'just in case'.
Flintmere: We detect image-only size charts via image-to-text ratio and flag products pointing to a size-chart metafield that isn't structured. Recommended fix: a table metafield agents can parse row-by-row.
Check · Data consistency
GTINs missing across the supplier-sourced long tail
Your hero line and own-design products carry GS1 barcodes. The dropship and supplier-basic SKUs don't. Your hero products rank on Google Shopping, the long tail is silently delisted — and the long tail is often a third of catalog revenue.
Flintmere: We flag every variant missing a barcode, validate the checksum on the ones you have, and group the missing ones by supplier — so you can route the GS1 UK purchase decision to the supplier relationships that matter.
Check · Product IDs
One product with six colours grouped as variants
Your 'Rainbow Pack T-Shirt' lists six colours as variants. Tidy for your ops team; invisible to agents. A shopper asking 'black t-shirt' gets none of your six because the product itself has no colour — only its variants do, and they're buried.
Flintmere: Flintmere detects multi-colour variant groupings and flags them against the agent-best-practice pattern (one product per colour, shared size range). We show you which SKUs this applies to before you decide whether to restructure.
Check · Structured attributes
Seasonal products still ACTIVE after the season ended
380 summer 2025 products are still status=ACTIVE with zero stock and policy=deny. Agents crawl them, send shoppers to dead pages, and your domain's agent-score quietly drops. It looks like a catalog that isn't maintained, so agents prefer one that is.
Flintmere: Flintmere cross-checks product status × inventory × policy × publish date and gives you a ranked list of products that should be DRAFT, archived, or restocked. The dead-page rate is one of the fastest-moving levers on agent trust.
Check · Data consistency
Target-gender metafield empty on unisex-branded lines
Your streetwear line is marketed 'for everyone'. The Shopify target_gender metafield sits empty on all 240 products. Agents filtering 'men's hoodies' or 'women's oversized tees' skip your entire streetwear catalog — the field can't be inferred, it must be set.
Flintmere: We check every product for target_gender using Google's spec values (unisex | male | female) and flag both missing fields and non-standard strings ('mens', 'womens' — both rejected by Google Merchant Center).
Check · Google category match
Concierge audit for apparel
Send your shop URL. Three working days later, a written audit lands in your inbox.
£97 gets you the team reading your catalog product by product: a 1,500-word letter pointing at specific SKUs by name (apparel teams get the size-chart and colour-token reads as standard), a per-product fix CSV with the worst ten already drafted — title, structured size, structured colour, composition — a 30-day fix sequence, the right GS1 UK route for your supplier mix, and a 30-day re-scan. No video, no call, no slide deck.