A vision model reads your product images and supplier documents and fills the attributes your taxonomy expects, with a confidence score on each one. High-confidence tags go straight in, uncertain ones queue for a person, and new arrivals are tagged on ingest instead of waiting for someone to get to them.
A product with no colour or material never appears under a filter, so it does not sell, so nobody prioritizes tagging it. The loop is self-sealing and quietly expensive.
Every new supplier drop arrives faster than the team can process it. The backlog only grows, and the freshest products are the worst covered.
Navy, dark blue and blue-navy become three filter values, splitting the same products into three groups and making the filter look broken to a shopper.
Your attribute list and allowed values per category. Tags that do not match your controlled vocabulary are useless downstream, however accurate they look on their own.
Vision handles colour, pattern, shape and visual style; supplier PDFs and spec sheets carry material, dimensions and compliance data. Combining both fills far more fields than either alone.
Every tag carries a score. Above your threshold it applies automatically, below it queues for review, and every correction feeds back so the threshold can be raised over time.
Free text is mapped to your vocabulary — navy becomes the one value you actually filter on — and new values are proposed for approval rather than created silently.
New products are tagged as they arrive so the backlog stops growing; the existing catalog is backfilled in batches, highest-traffic categories first.
We build in your stack rather than moving you onto ours. The list below is what this solution most often connects to — other systems are a scoping question, not a blocker.
Visual attributes like colour, pattern and shape are strong; fine material distinctions are weaker and usually come from documents instead. We measure per attribute on your own catalog before rollout.
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