Commerce (NASDAQ: CMRC) has introduced two catalog-enrichment offerings intended to help B2B and B2C merchants turn product records into structured data for AI-driven discovery. For enterprise commerce teams, the immediate value is less manual catalog work and a more consistent product-data foundation as search, storefront and conversational buying channels increasingly rely on machine-readable information.
The company said Feedonomics Enrichment and BigCommerce Catalog Enrichment create product titles, descriptions, feature bullets, frequently asked questions, SEO metadata and structured fields from existing catalog information. The products are part of Commerce’s agentic-commerce portfolio, which it positions as a way to prepare product information for AI-assisted shopping and internal commerce workflows.
Product data is usually treated as a back-office requirement, but that assumption is becoming more expensive. A buyer or software agent cannot reliably compare or recommend an item when the catalog lacks consistent attributes, relevant descriptions or accurate contextual details. That makes catalog quality a practical constraint on both customer discovery and the information that internal commerce systems can use.
Feedonomics Enrichment is available now for English-language content, according to the company. It is aimed at enterprise and multichannel brands that need to prepare catalog information for storefronts, search and marketplace destinations. The service can be used in self-managed or managed-service models and includes a quality scorecard that Commerce says evaluates accuracy, consistency and adherence to a brand’s inputs. The company also said it provides an issue list for records that may need attention.
BigCommerce Catalog Enrichment is offered in early access. It places a similar bulk-generation workflow inside the BigCommerce control panel, where merchants can select products, supply brand inputs, review generated material and apply approved changes. Commerce said the workflow is designed to let teams enrich thousands of products without exporting a catalog to a separate tool and importing it again.
That review step matters. Generative tools can accelerate repetitive content work, but catalog changes affect customer-facing facts, search visibility and downstream integrations. The company says merchants retain control over what is published; organizations evaluating the tools should still set clear approval rules and measure output quality against their existing product-information standards.
The product-data issue also reaches beyond retail marketing. A large merchant may distribute the same records to a B2B portal, a direct storefront, a marketplace feed and internal teams. Keeping attributes aligned across those destinations can become a data-governance problem, especially when product lines, policies or pricing change. A controlled enrichment workflow can reduce that reconciliation burden, but it does not replace ownership of the underlying source data.
The launch also separates current availability from planned capability. Commerce said a conversational reporting experience is planned for Feedonomics Enrichment, while multilingual product-content generation is expected later. The source does not provide pricing, performance benchmarks or a timetable for those additions, so buyers will need to assess the products against their own catalog complexity and governance requirements.
For CIOs and commerce leaders, the strategic issue is not simply whether AI can write a better product description. It is whether the organization can maintain product data that works consistently across storefronts, marketing systems and emerging agent interfaces. Commerce’s approach gives merchants a workflow to create and review that data in bulk. Compared with legacy processes built around spreadsheets, exports and separate agency projects, the potential shift is a more governed, reusable catalog layer rather than a collection of one-off channel updates.