Turning Furniture Complexity into Structured Commerce

CASE STUDY

Turning Furniture Complexity into Structured Commerce

A large Romanian furniture retailer operates at scale, with a broad catalog spanning furniture, décor, and interior solutions across every room. Through physical showrooms and a robust e-commerce platform, Expomob serves customers with diverse styles, configurations, and product variants.

With 15 major categories and dozens of subcategories, the retailer’s catalog reflects the real complexity of furniture retail: each product type comes with its own measurements, materials, variants, and visual requirements — all of which must be accurately presented online to support confident buying decisions.

The Challenge

Furniture is an infrequent, high-consideration purchase. Customers return multiple times, compare across retailers, and seek reassurance at every step. To decide with confidence, they need clear specifications, accurate dimensions, and visuals that help them imagine how a product will live in their space.

For this retailer, this challenge unfolded on two fronts:

Complex product data

Supplier data arrived in different formats and languages, with inconsistent naming, overlapping attributes, and category-specific requirements. A sofa demands one attribute model; a kitchen set, another. Within the same catalog, filters ranged from 10 to 30+ attributes, all of which needed to be normalized, translated, and structured for comparison.

A growing visual bottleneck

Each product variant — color, fabric, or finish — required dedicated images. Suppliers often provided only basic studio shots, forcing companies to rely on costly photoshoots or manual editing to create lifestyle visuals that convert. As SKUs increased, visual production slowed time to market and limited how accurately products could be presented online.

In furniture commerce, missing data or unconvincing visuals erode trust — and returns are expensive.

The Solution

What This Retailer Needed

Starting with the sofas category, it turned to Dyver to:

Normalize and translate supplier data into Romanian, website-ready fields
Generate structured titles, categories, descriptions, and attributes
Produce SEO-optimized product content
Enable accurate filtering and comparison
Reduce reliance on manual visual production

The Dyver Approach

Dyver unified the retailer’s product data and visuals into a single, scalable workflow.

From 32 mixed-language supplier fields, Dyver generated 47 clean, Romanian, website-ready fields, using AI-driven writing, translation, extraction, and mapping:

Generated structured Romanian product titles based on key attributes
Created standardised product categories using functional criteria
Translated and normalized supplier attributes
Extracted new, filterable attributes from raw text
Generated SEO-ready HTML product descriptions
Created image titles aligned with naming logic
Mapped all data to the retailer’s website structure — from metadata to images

In parallel, Dyver’s AI-powered image enrichment transformed a single product image into multiple market-ready visuals — adapting colors and materials, placing products into realistic environments, and adding contextual elements like rooms or people.

The result: one structured source of truth for data and visuals — ready to publish.

The Outcome

With Dyver, the furniture retailer gained:

A clean, consistent product intelligence layer that supports long decision cycles
Faster onboarding of supplier products
Accurate filtering, comparison, and discovery for customers
Reduced visual production costs and time to market
Stronger foundations for SEO, trust, and conversion

Most importantly, customers could make decisions with confidence — supported by structured data and contextual visuals that reflect how furniture is actually chosen.

From fragmented supplier inputs → to structured product intelligence → to clarity at scale.