Smartphone-based 3D body scanning for any body type

Fit:Match uses the smartphone camera to capture a user's body in 3D, requiring only a front and side photo in fitted clothing. The AI processes over 200,000 data points to create a precise digital twin. This eliminates the need for expensive 3D scanners or manual measurements.

Business outcome: Enables any shopper with a smartphone to create an accurate fit profile in under 60 seconds, removing the primary barrier to size recommendation adoption.

AI-driven size recommendation across brands and styles

The platform matches a user's 3D body model against a brand's specific size charts and garment measurements. It accounts for fabric stretch, cut, and intended fit (slim vs. relaxed). The recommendation engine learns from purchase and return data to improve over time.

Business outcome: Delivers a personalized size recommendation with high confidence, directly reducing size-related returns and increasing first-time-right purchases.

API and Shopify-native integration for seamless deployment

Fit:Match offers a REST API for custom integrations and a dedicated Shopify app for merchants on that platform. The tool can be embedded at the product page, cart, or checkout stage. Data flows into existing order management and analytics systems.

Business outcome: Reduces technical implementation time from months to days for Shopify merchants, and provides enterprise teams with a flexible API for custom workflows.

Return reason analysis and fit intelligence dashboard

Fit:Match aggregates fit-related return data across all users, providing brands with insights into which styles, sizes, or body types have the highest return rates. This data can inform production planning, size grading, and marketing decisions.

Business outcome: Transforms return data from a cost center into a strategic asset for product development and inventory planning.

Privacy-first body data handling with no image storage

The platform processes images locally on the device and converts them into anonymized 3D body measurements. No identifiable photos are stored on servers. This approach aligns with GDPR and CCPA requirements.

Business outcome: Mitigates privacy liability for brands and builds shopper trust, which is essential for adoption of body-scanning technology.

Multi-brand fit profile for cross-retailer use

Shoppers create a single Fit:Match profile that works across any participating retailer. The platform stores the 3D body model and applies it to each brand's unique size data. This creates a network effect: the more brands join, the more valuable the profile becomes.

Business outcome: Creates a sticky consumer habit and reduces the need for each brand to build its own fit solution from scratch, lowering total cost of ownership.

• Fit accuracy for diverse body typesThe 200,000+ data point scan captures nuances that traditional size charts miss, making it effective for plus-size and petite shoppers.
• Privacy-first architectureOn-device processing and no image storage address the primary consumer concern with body scanning technology.
• Multi-brand network effectA single shopper profile works across all partner retailers, increasing the value of the data for each brand over time.
• Return reduction ROIBrands using Fit:Match consistently report double-digit percentage reductions in size-related returns, directly improving margin.

• Requires user actionShoppers must complete a body scan, which adds friction to the purchase journey and can drop conversion for less motivated buyers.
• Limited to apparel with defined sizingThe tool is less effective for unstructured garments like scarves, bags, or one-size-fits-all accessories where fit is not a variable.
• Dependent on brand size data qualityIf a retailer's size charts or garment measurements are inaccurate, the recommendation engine cannot compensate for bad input data.
• Professional RealityFit:Match is a powerful tool for reducing returns, but it is not a substitute for good product photography, accurate size charts, or a generous return policy — it works best as part of a holistic fit strategy.

High-return apparel categories

Brands selling denim, dresses, and tailored outerwear — categories with return rates exceeding 30% — use Fit:Match to provide size confidence at the point of purchase. The 3D scan data directly addresses the primary reason for return in these segments.

Size-inclusive and extended-size brands

Retailers serving plus-size and petite customers benefit from Fit:Match's ability to map non-standard body shapes to available sizes. This reduces the frustration of limited size guidance for underserved demographics.

DTC brands with high customer acquisition costs

For direct-to-consumer brands where each new customer represents a significant ad spend, Fit:Match increases the probability of a first purchase fitting correctly, protecting the ROI of acquisition campaigns.

Marketplaces and multi-brand retailers

Platforms selling products from dozens of brands use Fit:Match to standardize sizing across their catalog. Shoppers create one profile that works for every brand, simplifying the cross-brand shopping experience.

Sign up for a Fit:Match business account and receive your API keys or Shopify app installation link.

Integrate the Fit:Match SDK into your product page or checkout flow — the Shopify app requires minimal configuration.

Upload your brand's size charts and garment measurement data to the Fit:Match dashboard for each SKU.

Launch the fit widget on your store, monitor the analytics dashboard for return rate changes, and iterate on placement to maximize scan completion rates.