Electronics and appliance ecommerce teams
Teams comparing products across Google Shopping, Amazon, Walmart, eBay, supplier feeds, and their own storefront.
Identity Guard checks whether a record is safe to match, compare, merge, or keep separate. It is built for teams whose pricing, marketplace, catalog, or feed decisions break when similar records are treated as the same thing.
The best customers manage many records across channels and lose money when identity decisions are too loose.
Teams comparing products across Google Shopping, Amazon, Walmart, eBay, supplier feeds, and their own storefront.
Teams dealing with duplicate listings, wrong detail-page matches, variant confusion, and seller-submitted data quality issues.
Teams that already move product data but need a safer decision layer before matching, comparison, or repricing.
These are not RAG evidence failures. They are commerce and data-matching failures that need a different presentation and buying motion.
Retailer SKU can be duplicated, platform-specific, variant-specific, or missing the proof needed for final same-product treatment.
Model suffix, region, generation, OS, panel size, bundle status, and condition can change the commercial decision.
A false same-product match can push a pricing, feed, or marketplace decision against the wrong item.
Start with a small proof sprint. Build a managed layer only when the sample proves material identity risk.
One narrow category or workflow, 25 to 75 masked match or comparison examples. Designed to prove whether wrong-match, duplicate, weak identifier, or unsafe comparison risk is real.
Deeper review of one category or source route with more examples, source proof requirements, unsafe comparison patterns, and a practical remediation path.
Scoped review of a larger identity export, category decision rules, exact-model evidence, suffix, size, generation, OS, bundle, condition, and source proof requirements.
API or batch decision layer that routes records into safe match, verify, review, or hold with explainable reasons and regression checks.
The first step is a fit call, then a $2,500 Proof Sprint on a scoped export: product/listing identifiers, titles, model fields, source channel, price fields if relevant, and any known match labels.