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Adobe Commerce · Fashion & Apparel

Fashion on Adobe Commerce, at catalog scale.

Size-and-color variant explosion, season-by-season re-merchandising, and performance at catalog depth are where fashion outgrows simple platforms. We engineer Adobe Commerce for that scale.

The problem

Where fashion & apparel brands get stuck

The patterns recur. Adobe Commerce solves some by default; the rest is engineering.

Challenge

Variant explosion

Size x color x fit multiplies SKUs fast; catalog, indexing, and search all strain under it.

Challenge

Seasonal merchandising

Re-merchandising every season by hand does not scale across a deep catalog.

Challenge

Performance at depth

Big Adobe Commerce catalogs need real tuning to stay under 2s at peak.

How it fits

The Adobe Commerce stack, tuned for fashion & apparel

Commerce stack · cross-section
REQUESTExperience tierPWA / Hyvä · Lookbook · Size guide · MobileCommerce APIsCatalog · Cart · Checkout · PromotionsContent & searchCMS · Live Search · MerchandisingData foundationPIM · OMS · InventoryAI & opsMerchandising AI · Search relevance · Returns triage

What we build

Adobe Commerce for fashion & apparel, engineered to fit

Build

Catalog architecture

Configurable products, attributes, and search structured for fashion depth and frequent change.

Build

Merchandising tooling

Rules and automation so re-merchandising is configuration, not a seasonal rebuild.

Build

Performance tuning

Varnish, Redis, and Elasticsearch tuned for sub-2s LCP at catalog scale.

The AI layer

AI that moves the numbers for fashion & apparel

Not AI bolted on the side — agents and models wired into the fashion & apparel workflows that actually decide revenue, with human approval and an audit trail.

The AI layer

Merchandising AI at scale

AI ranks category pages by margin and conversion across a deep catalog, so merchandisers approve instead of hand-sorting thousands of SKUs.

Search relevance

Learning-to-rank and synonyms tuned on your data so fashion search understands fit, occasion, and style language.

Returns triage

Agents read return reasons, surface fit and quality patterns, and route exchanges to protect margin.

Where we built

Engagement record

Built for fashion at catalog scale — deep variants, frequent seasons, and peak that cannot stutter.

40%Faster Page Loads

Read the case study

Questions

Fashion & Apparel on Adobe Commerce, answered

Why Adobe Commerce over Shopify for fashion?
When variant depth, complex promotions, B2B, or multi-brand catalogs outgrow Shopify's model, Adobe Commerce earns it. If they don't, we'll say so — platform follows the catalog, not the other way around.
How do you keep a deep catalog fast?
Full-page cache with Varnish, Redis sessions, Elasticsearch/Live Search tuning, and image discipline — measured at p75, not on a dev laptop.
Can re-merchandising be automated?
Yes — rule-based and AI-assisted category sorting so each season is configuration and review, not a manual rebuild.
PWA, Hyvä, or Luma theme?
Hyvä for speed with a Magento-native team, PWA Studio or headless when you want a decoupled front. We pick per your team and performance bar.

How an engagement starts

Three steps to a partnership

01

Intake call

30 minutes. We listen, you talk. No deck.

02

Diagnostic

We audit the surface, name the bottleneck, propose a path.

03

Kickoff

Senior engineer in your standup by week two.