Add Luna.io eyewear shopping technology to your BigCommerce store to power virtual try-on, guided fit, and prescription-ready product configuration that reduces errors and returns.
• BigCommerce product catalog data (SKUs, options, pricing, images, and attributes) is synced to Luna.io to power virtual try-on, fit logic, and guided eyewear selection.
• Frame, lens, and prescription-related attributes are mapped to Luna.io’s configuration model so vision-specific choices stay tied to the correct product and variant.
• Customer-selected prescription inputs and lens options are captured in the Luna.io flow and passed back to BigCommerce as line item metadata for order fidelity.
• Add-on components (such as lens packages or coatings) are represented as variant selections or separate SKUs, depending on the BigCommerce catalog model in use.
• Checkout remains native to BigCommerce, while Luna.io renders the try-on and configuration UI on PDPs and related selection steps.
• Sync jobs support full catalog loads and delta updates, with validation and logging for rejected records and mismatched option sets.
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We embed Luna.io widgets on your BigCommerce PDP and map them to your frame SKUs, variants, and lens options. The try-on and fit flow stays tied to the exact product configuration the shopper is building.
Yes, we configure the Luna.io Rx flow to collect required prescription fields and lens choices, then pass the selected configuration into BigCommerce line items. This helps prevent incomplete orders and reduces manual follow-up.
Typically we sync frame identifiers, size and fit attributes, variant rules, and lens add-on logic. If your catalog data is messy, we can also normalize attributes so guided selection behaves predictably.
It can, as long as your BigCommerce store structure and product catalogs are clearly separated by channel or locale. We align Luna.io configuration per storefront so fit guidance and Rx requirements match each market.
Most projects start with a short discovery to confirm SKU structure, Rx requirements, and UX placements, then move into implementation and QA across devices. Go-live timing depends on catalog complexity and the number of lens rules.










