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First in a series of articles on how to incorporate stronger AI-driven commerce solutions
Nearly every commerce team we talk to has a version of this very same catalog problem: The catalog is technically live, and products are listed. The channel feeds seem to be running smoothly. But somewhere in the gap between “technically live” and “performing well,” a lot of margin is quietly disappearing.
Just as most commerce teams experience this, they also usually have the same root cause: bad product data. Things like missing attributes, miscategorized SKUs, incomplete descriptions, and values that don’t meet channel requirements.
It’s a common problem, but fixing product data at scale is often relentless manual work. And the teams responsible for it have other things to do.
That’s the problem RithumIQ’s was built to solve.
Your product data is a revenue problem
Your instinct may be to treat catalog hygiene as an ops issue, or something that gets cleaned up in a quarterly data audit.
But every SKU with a missing product weight creates a shipping estimation problem. Every product in the wrong category reduces agentic discoverability. Every listing suppressed for missing attributes is a hurt product sale. Multiply those issues across hundreds of thousands of SKUs and dozens of channels, and what looks like a data maintenance task starts showing up in your P&L.
Solving this manually doesn’t scale. You can’t audit 400,000 SKUs in any reasonable timeframe, and even if you could, the channels are constantly changing their requirements. It’s an unwinnable race.
Two features from Rithum’s one AI-driven solution
RithumIQ, the AI engine at the heart of Rithum, addresses the unwinnable race issue through two features that work on different ends of the same problem.
Catalog Assist handles the content side. It uses AI to detect missing or invalid product attributes and generates suggestions to correct them, based on your existing product data and channel-specific rules, not generic recommendations. For a product with incomplete color values, an incorrect size designation, or a description that fails a channel’s character count threshold, Catalog Assist surfaces the issue and proposes a fix. Crucially, it keeps humans in the loop: every suggestion requires approval before it goes live, and teams can edit directly within the Rithum platform. The goal is to remove the part of their job that’s purely manual, so your team focus on the decisions that actually require judgment.
For retailers, Catalog Assist solves a specific version of this problem in the supplier onboarding flow. New supplier products often arrive with catalog gaps that stall activation. Catalog Assist generates suggestions to fill those gaps and speed up the path to live listings by compressing a process that normally takes days into something that can happen in hours.
Magic Mapper handles the categorization side. Getting products into the right category on each channel sounds like a solved problem. It isn’t. Different channels have different taxonomies, different hierarchy depths, and different naming conventions. A brand managing presence across 20 marketplaces is essentially doing 20 categorization jobs. And the volume of SKUs means the work scales with the catalog, not with the team.
Magic Mapper automatically categorizes products across multiple channels simultaneously, and at scale. Teams can review recommendations, filter results, and selectively approve changes in bulk before anything goes live. It replaces hours of manual categorization work with a review step that takes minutes, giving you and your teams the confidence that comes from knowing nothing was pushed live without human sign-off.
The power of RithumIQ
Both capabilities are part of RithumIQ, Rithum’s AI intelligence engine, which was built on one of the world’s richest and most comprehensive commerce datasets: more than $50B in GMV annually, 4B+ SKU transformations daily, and 500M+ listing updates every day.
That scale is what gives us the best basis to ensure your product data quality is clean and actionable for all your automations. Catalog Assist and Magic Mapper aren’t drawing on a generic product knowledge base. They’re drawing on what correct attribute formats actually look like for a given category and what categorization patterns work on which channels. These recommendations are informed by how ecommerce actually operates at scale.
Rithum IQ’s suggestions are grounded in data that your team doesn’t have access to on their own. Three out of four Rithum clients accept RithumIQ’s recommendations 99% of the time.
The bigger picture of product data
The emergence of AI-powered shopping, or agentic commerce, is raising the stakes on clean, accurate, complete product content in a new way.
Rithum and Retail Dive’s recent consumer research found that 58% of shoppers blame the brand when an AI assistant gives them wrong product information. Simply put, if your product data is incomplete or inaccurate, it doesn’t just hurt you in search terms. It hurts you every time a shopper asks ChatGPT, Gemini, Perplexity, Claude, or any other agentic commerce tool to find them the best in your category. And it especially hurts you when AI answers give your shoppers a competitor’s product instead of yours, simply because their data is cleaner.
Catalog Assist and Magic Mapper are tools for today’s operational problems. They’re also the infrastructure layer for what comes next. An agentic shopping environment requires catalogs that are already in good shape, with accurate attributes, correct categories, complete content, and channel-compliant data across the board. The work to get there can get done faster with RithumIQ. It’s not AI as a feature announcement, but AI as the foundational layer behind your commerce initiatives.
