SKULaunch takes incomplete, unstructured product data - from suppliers, existing catalogues, or legacy systems - and transforms it into enriched, attribute-complete, channel-ready content at scale.
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Product catalog enrichment software is a category of tool that takes incomplete or unstructured product data and adds structured attributes, accurate descriptions, and consistent formatting - at scale. The goal is to transform raw or partial product records into complete, publish-ready listings that work for search, for filter, for marketplaces, and for customers.
For retailers and distributors managing thousands of SKUs across dozens of suppliers, manual enrichment isn't a strategy — it's a permanent backlog. SKULaunch is the AI-powered catalog enrichment platform that extracts structured attributes, generates product descriptions, and fills data gaps automatically — across any product category, at any scale, with your team governing by exception rather than doing everything by hand.
Technical attributes (voltage, dimensions, material, weight, IP rating), product descriptions and titles, category taxonomy and classification, image metadata and alt text, search keywords, and marketplace compliance data.
Incomplete product data directly damages ecommerce performance. Filters that don't work. Search that returns nothing. Product pages that don't convert. The enrichment gap is not a nice-to-have problem — it's a revenue problem.
Traditional enrichment was manual — someone read a spec sheet and typed values into a PIM. At 50,000 SKUs, that takes months and costs £300–400k per year in staff time. And it never catches up with new supplier data arriving continuously.
Attributes extracted from PDFs, images, URLs, and raw text automatically. Descriptions generated from verified data — not hallucinated specs. Entire catalogues processed overnight. Your team governs the exceptions, not every record.
Product catalog enrichment isn't one thing — it's three distinct operations that most tools handle separately. SKULaunch does all three in a single pipeline.
SKULaunch extracts structured attribute values from any source — supplier PDFs, product URLs, spec sheets, images, raw text, and existing descriptions. Voltage, dimensions, weight, material, IP rating — any attribute your schema requires.
Once attributes are structured and verified, SKULaunch generates product titles, short descriptions, feature bullets, and long-form product copy — built from real enriched data, not hallucinated specifications. Every claim is traceable to a verified source.
SKULaunch suggests and applies category classification for every product — mapping to your internal taxonomy or to external standards like ETIM, GS1, or marketplace-specific category trees. Consistent classification is what makes faceted search work.
No six-month implementation. No PhD in data science. Your first batch of enriched product data is ready within 48 hours of setup. Run enrichment on demand — across your full catalogue or a specific supplier batch.
Upload existing catalogue data, connect your PIM, or have suppliers submit via portal. SKULaunch ingests any format — CSV, Excel, PDF, product URL, image, or data feed. No reformatting before import.
AI agents read every product record, extract structured attributes, fill gaps using web research, and generate descriptions from verified attribute data. Confidence scores on every extraction. Entire catalogue processed overnight.
High-confidence enrichments are approved automatically. Low-confidence extractions, missing required attributes, and format issues are routed to your team for review. You work through exceptions — not every record.
Approved, enriched data is pushed directly to your PIM, ecommerce platform, marketplace, or ERP. SKULaunch integrates with Akeneo, Shopify, Plytix, Magento, and Mirakl — formatted to each destination's requirements.
Whatever the context, the underlying need is the same: product catalog enrichment software that handles volume, handles
technical complexity, and fits into your existing workflow without a six-month implementation.
Managing large supplier networks where product data arrives in inconsistent formats and manual enrichment creates a permanent backlog.
With technical product catalogues — electrical, industrial, HVAC, building supplies — where attribute completeness drives search and filter performance.
Onboarding new sellers whose product data doesn't meet listing compliance requirements. Enrich at intake before anything goes live.
With empty product pages and a go-live deadline. The PIM is live — the data isn't. SKULaunch fills the gap without a re-implementation.
Launching digital channels for the first time with a back-catalogue that was never properly structured. Enrich the backlog once, govern it forever.
Managing ongoing catalogue quality across multiple systems. Completeness scoring, exception workflows, and audit trails built in.
The economics of product catalog enrichment changed fundamentally when AI moved from generating content blindly to extracting and verifying attributes from source data first. Here's what that shift looks like in practice.
Managing large supplier networks where product data arrives in inconsistent formats and manual enrichment creates a permanent backlog.
With technical product catalogues — electrical, industrial, HVAC, building supplies — where attribute completeness drives search and filter performance.
Onboarding new sellers whose product data doesn't meet listing compliance requirements. Enrich at intake before anything goes live.
Operations Director, UK industrial and electrical distributor
SKULaunch customers are ecommerce teams and product data managers who have tried the manual route — and know it doesn't scale past 5,000 SKUs.
Trusted by
APS Industrial
Mole Valley Farmers
RS Group
Bowens Australia
Maxiparts
Extracts structured attributes from PDFs, product URLs, images, spec sheets, and raw text. Any source format, any product category.
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Generates product titles, short descriptions, and feature bullets from verified attribute data — not hallucinated specifications. In your brand voice.
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Applies category classification to every product — mapping to your internal taxonomy or to ETIM, GS1, and marketplace-specific category trees.
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Normalises supplier data from any format to your internal schema automatically. 200 supplier formats become one consistent dataset. No cleaning rules to write.
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Confidence score on every extraction — high-confidence values approved automatically, low-confidence values routed to your team for review. Nothing publishes without sign-off.
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Processes 80,000+ SKUs in a single overnight run. AI runs while your team sleeps — completeness scores and exception reports ready in the morning.
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Integrates directly with Akeneo, Shopify, Plytix, Magento, and Mirakl. Enriched, approved data pushed to your destination — no manual export or reformatting.
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Tracks completeness by supplier, category, and attribute — in real time. Flags gaps. Controls publishing. You know exactly where the data holes are before they reach customers.
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The AI enrichment pipeline in detail — confidence scoring, overnight runs, exception review.
Read More →How SKULaunch handles the volume, supplier count, and technical complexity of B2B distribution.
Read More →A plain-English guide to SKU enrichment — what it means, why it matters, and how to do it at scale.
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Product data enrichment that actually gets done — so your product pages convert, your search works, and your team stops living in spreadsheets.
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