Do we need a huge dataset for recommendations to work?

No. Even a modest catalogue and order history is enough to beat generic "customers also bought" widgets, because the matches are based on meaning rather than raw co-purchase counts.

Can this help in-store and not just online?

Yes. The same inventory and product intelligence feeds staff tools and screens, not only the website.

AI automation for retail

Retail blends the physical and the digital, and AI helps most where the two meet: knowing what to stock, helping a shopper find the right thing, and answering the same product questions without tying up staff.

Where AI actually lands in retail

Inventory signals

Models that spot which lines are about to run short or sit dead let buyers act on a signal instead of a hunch.

Recommendations that fit

Similarity-based recommendations (the same embeddings that power search) lift basket size by surfacing the genuinely related item, not a random one.

Product questions, answered instantly

Fit, compatibility, and availability questions are answerable from your catalogue the moment they are asked, online or on the floor.

Why now: the market is moving

Third-party platform data, not our own results:

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