From “something like this” to the right product.

Marqo-FashionSigLIP represents fashion images and descriptions for similarity search. You can compare a reference image with catalogue garments. Combine results with size, category and availability filters; the model does not maintain your store’s stock.

TimesFM 3.0 supports univariate and multivariate forecasting with covariates. You can study SKU history using its format, but the linked weights have a non-commercial, non-production licence. We do not present them as a licensed option for replenishment or commercial decisions; that use requires Google’s authorisation.

Retrieve similar products by image, description or attributes and filter candidates using current stock. Your application can respect price, brand or size rules. Evaluate whether alternatives preserve the features behind the query as well as being available.

Expose authorised tools to query the relevant order or policy. The application identifies the user and validates accessible data. The assistant communicates results and explains steps; order changes should follow your store’s confirmations and rules.

Search uses product and query representations; forecasting uses numerical history. You can connect both in your application, but evaluate them differently: result relevance for search and error by horizon for forecasts. An LLM can support conversations about the catalogue.

Account for embedding generation, the index, catalogue updates and queries. An assistant adds LLM inference. Test a representative category and estimate traffic to compare configurations without sizing the entire catalogue from day one.

Your integration can query the catalogue and APIs exposed by your systems. QDivZero runs compatible models; your application connects results to products, prices and stock. Define the data each task needs and which system maintains current information.

You can start with similarity between catalogue descriptions and attributes, without individual history. If you later add behavioural signals, your application decides how to use them. Compare both approaches for relevance and availability before adding more data.

Track searches with no results, relevance of the first products, clicks and progression towards purchase. Compare equivalent queries with the previous search engine. Also review returns or incorrect variant selections so increased interaction does not hide unhelpful recommendations.