By Mac (Mohammed Ali Chherawalla), Co-founder, Wednesday Solutions
Your e-commerce catalog team onboards 500 new SKUs from a new brand partner on Monday. By Wednesday every product has a category, cleaned attributes, SEO-ready title and description, and a quality score. No data entry team worked overtime. No catalog manager spent the week in spreadsheets. The products are live and searchable.
That's AI-powered catalog operations inside an e-commerce company. Onboarding speed and catalog quality stop being in tension with each other.
E-commerce catalog operations are the invisible bottleneck in most marketplaces and direct brands. The buying team closes a new vendor. The vendor sends a product feed in their own format, with their own taxonomy, missing half the attributes the site requires. A catalog team manually cleans, remaps, and enriches every SKU. The process takes days per vendor. During peak season, the queue grows faster than the team can clear it. Products launch late. Search doesn't surface them correctly. Conversions suffer from missing attributes customers filter on.
The catalog team is doing work that has deterministic rules. Most of it can be automated.
The 5-stage ladder
Stage 1: Manual data entry. Catalog team receives vendor feeds, manually maps to site taxonomy, fills missing attributes, writes descriptions. High effort per SKU, inconsistent quality across the team.
Stage 2: Feed normalization. Automated mapping from vendor formats to site taxonomy for common feed types. Catalog team handles exceptions and enrichment. Data entry load drops for repeat vendors.
Stage 3: AI attribute extraction and enrichment. Missing attributes extracted from product titles, descriptions, and images automatically. Category assigned by AI. Quality score flagging items that need human review. Catalog team works the flagged items, not the full queue.
Stage 4: AI-generated copy. Product titles and descriptions generated for SEO and conversion, based on attribute data and category benchmarks. Catalog team reviews and approves. Content quality becomes consistent at scale.
Stage 5: Catalog intelligence. The system identifies which attribute completeness patterns correlate with conversion and search rank in your specific catalog. Recommendations surface automatically — which product types need richer descriptions, which attribute gaps are costing you search visibility.
What each stage unlocks
Stage 3 compresses vendor onboarding from days to hours for clean feeds. The buying team stops waiting on catalog ops to clear the queue.
Stage 4 makes content quality consistent. An AI-generated title optimized for your category's search patterns outperforms a manually written one in most cases because it's based on what ranks, not what sounds good.
Stage 5 turns catalog ops from a data cleaning function into a conversion optimization function.
Wednesday Solutions and e-commerce
Wednesday Solutions has built mobile and platform engineering for Zalora, one of Southeast Asia's largest fashion e-commerce platforms, handling catalog operations and iOS and Android development at scale. Wednesday has also worked with PharmEasy on e-commerce platform engineering. Catalog automation requires taxonomy management, NLP enrichment, image processing, and a QA workflow the catalog team can run without raising tickets.
Lucy Lai, Associate Engineering Director at Zalora:
"We're most impressed with Wednesday Solutions' flexibility."
Where to start with Wednesday
Two-week fixed-price sprint. Wednesday maps your current vendor feed formats, taxonomy structure, and catalog team workflow. By day 14: automated attribute extraction running on one product category and AI-generated copy live for a sample of SKUs.
Fixed price. Money back if the sprint doesn't deliver a working catalog automation pipeline by day 14.
Talk to the Wednesday team about your catalog onboarding backlog. They'll show you how much of your team's queue is automatable before you commit to anything.
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