Desynt Digital Solutions
Brand-Led Storefront with AI Fit Matching
Index of work
E-CommerceFashion & Lifestyle2026

Brand-Led Storefront with AI Fit Matching

Synergy Mall

26
4
Fit measurements captured
3
Filter dimensions on collection
App Router
Next.js architecture
Pre-launch
Build stage
Duration
7 weeks
Team
2 people
Discipline
E-Commerce

Synergy Mall sells modern wardrobe essentials on a promise of precision — "the only luxury we permit." A promise like that has to be felt in the interface before a customer believes it about the clothes. The build is deliberately restrained: architectural typography, a lot of space, and one genuinely differentiating feature — sizing that doesn't ask you to guess.

Next.js App RouterReactTailwind CSSnext/imageLiteSpeed on Hostinger

01 — The brief

The challenge

Returns are the tax on selling clothes online, and almost all of them come down to fit. Synergy Mall needed a storefront that could carry a quiet-luxury brand position without the visual noise most commerce templates impose, and that could tackle sizing head-on rather than shipping another generic size chart. It also needed a content architecture the team could grow into — collection, categories and editorial — before the catalogue existed.

02 — What we built

The solution

We built the storefront on the Next.js App Router with an editorial design system: architectural type, generous space and a restrained palette carrying the brand position. The collection page ships with category, price-range and sort filtering ready for catalogue growth, a categories browser and an editorial journal give the brand somewhere to speak, and an AI fit flow captures height, weight, chest and waist to match a customer to a silhouette instead of a generic size chart.

03 — Method

How we got there

  1. 01Built the site on the Next.js App Router with an editorial, architecture-led design system
  2. 02Designed an AI fit flow capturing height, weight, chest and waist for silhouette matching
  3. 03Shipped a collection page with category, price-range and sort filtering ready for catalogue growth
  4. 04Built a categories browser and editorial journal so the brand has room to tell its story
  5. 05Optimised imagery through next/image with responsive srcsets across all breakpoints
  6. 06Structured the front end so products and editorial can be populated without a rebuild

04 — Proof

What changed

  • Editorial storefront carrying a quiet-luxury position without template noise
  • AI fit flow addressing sizing from four measurements instead of a generic chart
  • Filterable collection architecture ready for catalogue growth
  • Content architecture — collection, categories and journal — in place ahead of launch

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