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AI for ecommerce: recommendations, search, support

AI in ecommerce honestly assessed: semantic search that understands shoppers, recommendations that convert, support that scales, platform-native first, custom where the data earns it.

Three lanes, honestly assessed

Semantic search: the shopper types "warm jacket for rainy commute" and gets relevant products, not the keyword-miss of yesterday's search. AI search (vector/embedding-based, offered natively by Shopify and via apps/plugins for WooCommerce) reads intent instead of matching words. The prerequisite is clean product data, the structured-content principle applies: attributes filled, categories sane, descriptions written for humans. Recommendations: "customers also bought," personalized feeds, post-purchase suggestions, platform-native recommendation engines are mature and the low-effort first step; custom models earn their keep only at catalog and traffic scale where the native logic demonstrably underperforms. Support: order-status and product-question bots over your real data (the build-vs-buy matrix applies directly), with human escalation preserved, the checkout-triage page exists because support bots that promise payment fixes they cannot deliver erode trust fast.

The honest prerequisites, in order

  1. Clean catalog data. AI features amplify whatever state your product data is in, missing attributes, inconsistent categories and duplicate SKUs become confident wrong answers. The data cleanup is the first line of every quote.
  2. Traffic that justifies it. AI search on a store with twenty visitors a day is a hobby. The measurement-first rule applies: instrument the current search (zero-result rate, exit rate) so the improvement is provable.
  3. Platform-native first. Shopify's native AI search and recommendation features, Woo's established plugins, the app-traps math applies: platform-native before third-party apps before custom.

What builds are worth it, and what is fashion

Worth it: semantic search when zero-result rates are measurable and meaningful; support bots scoped tightly to order-status and FAQ (the chatbot service covers the build); recommendation widgets the platform already provides, configured properly. Fashion: AI-generated product descriptions at scale (the content-quality trap applies, mass unedited AI copy reads as mass unedited AI copy), "AI stylist" features without the data to power them, and chatbots bolted onto stores whose checkout is broken, fix the checkout first; no AI recovers a store that cannot take payment. The brief with your catalog state and current search data starts the honest assessment.

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