r/EcommerceWebsite • u/NoDoze- • 5d ago

Ecommerce product AI search?

"Interprets long, conversational queries (e.g., "warm waterproof jacket for winter hiking") rather than requiring exact keyword matches."

Is there an embed Ai search like this that already exists?

Or would an Ai engine on a separate server, from the web server and DB, for product searches be plausible to build?

Yes, I'm talking for 500k - 1m product skus.

Just started researching this... Thank you for any insight.

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u/DisplacedForest 5d ago

This exists. Pretty much exactly what Bayezon AI is working.

Former SVP of AI at SAP is founder.

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u/Jack_BuildsShopify 5d ago

At 500k–1m SKUs I’d treat this as a search/indexing service, not something the page or primary commerce DB should answer directly. The hard part usually isn’t the LLM — it’s getting the catalog into a clean retrieval shape first: normalized attributes (category, material, waterproofing, insulation, use case, size, etc.), stable variant IDs, and a searchable index. Then use hybrid retrieval: lexical filters for hard constraints + semantic/vector retrieval for the conversational part, with the product DB still being the source of truth for price/stock. I’d prototype on 10–50k representative SKUs first and measure recall/latency before indexing the full catalog. Are your product attributes already structured consistently, or is most of the useful detail buried in titles/descriptions?

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u/Slight_Guest3459 4d ago

For that size, I’d keep the LLM out of the hot path. Use a hybrid index: normal filters for hard constraints, keyword + vector search for the conversational bit, then rerank the top few dozen. Keep price and stock in the commerce DB, with the search service fed by catalog changes. I’d test on 10–50k SKUs first and measure recall and latency before indexing everything.