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Foundation Layer

Catalogue intelligence.

Your search, filters, SEO, recommendations, and AI shopping assistants are only as good as the product data underneath. Youzu fixes that layer in days.

Navy velvet mid-century sofa Enriched

Sofa · 3-seat

SKU-NV3

A timeless silhouette with modern comfort.

Style
Mid-Century Modern
Material
Velvet
Color
Navy Blue
Room
Living Room
Mood
Elegant · Cozy
Era
1960s-inspired

Multi-modal · SEO ready

Search, recs, 3D & more

+30%

Conversion lift

customer benchmark

+65%

AOV increase

basket expansion

48h

Audit turnaround

50-SKU sample

1M+

SKU-scale ingest

marketplace-ready

Sparse product feed enriched into structured catalogue intelligence

Raw feed

AI enrichment

Ready surfaces

Catalogue Intelligence

Turn catalogue chaos into one clean layer.

Thin feeds, inconsistent seller data, duplicate SKUs, and missing product context weaken search, filters, SEO, and recommendations. Youzu reads images and product fields together, then normalises each SKU into an enterprise-ready source of truth.

  • Ingests product images, titles, descriptions, and feed fields together.
  • Detects category, style, material, color, use case, and merchandising context.
  • Normalises values against your taxonomy instead of creating another free-text mess.
  • Prepares every SKU for search, filters, recommendations, SEO, and visual experiences.
Catalogue categories and attribute enrichment workflow
Attribute Enrichment

Enrichment with human-reviewable outputs.

The enrichment layer fills missing attributes and exposes the confidence signals merchandising teams need to approve, tune, and ship product data enrichment at catalogue scale.

  • Completes sparse attributes before products reach shoppers.
  • Surfaces confidence scores so low-certainty fields can be reviewed.
  • Supports localised labels, market-specific taxonomy, and seller normalisation.
  • Keeps merchandisers in control with editable structured outputs.
Visual embedding map for product intelligence
Visual Embeddings

Quality signals that power every surface.

Every SKU is encoded into visual and semantic embeddings, then scored for completeness, image quality, moderation risk, and confidence. The same representation powers discovery, recommendations, room intelligence, and content generation.

  • Unifies visual similarity, semantic meaning, product attributes, and shopper intent.
  • Flags catalogue gaps before they degrade search or recommendation quality.
  • Makes new products discoverable as soon as they are enriched.
  • Feeds downstream surfaces without rebuilding a separate model pipeline for each one.

One layer instead of a stitched visual stack.

Fix the product record before shoppers see it

Youzu fills sparse records, normalises seller feeds, groups variants, and scores catalogue health before weak data reaches search or merchandising.

  • Attribute completion from images and text
  • Category cleanup, variant grouping, and duplicate detection
  • Confidence scoring for human review

Outcome

48h

Before/after audit sample

Developers

Enrich once. Ship every surface.

Send images and basic SKU fields. Youzu returns structured attributes, confidence scores, embeddings, and downstream-ready product intelligence.

  • One endpoint per surface
  • SDKs and direct HTTP examples
  • Sub-500ms p95, 99.95% SLA
import { Youzu } from "@youzu/sdk";

const youzu = new Youzu({ apiKey: process.env.YOUZU_KEY });

// Visual search from any image
const results = await youzu.lens.search({
  image: imageUrl,
  filters: { inStock: true, priceMax: 200 },
  limit: 12,
});

// Room intelligence — place catalogue items into a photo
const scene = await youzu.rooms.fill({
  image: roomPhotoUrl,
  category: "sofa",
});
FAQ

Common questions

Get started

Ready to transform how your customers shop?

Most teams ship Youzu Discover in two weeks and see ROI in the first month. Book a 30-minute walkthrough on your own catalog.

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