Fast Simon has introduced a Commerce-Native AI model for eCommerce product discovery, aimed at brands using Shopify.

The model is designed to bring merchandising, personalisation and optimisation together in a single system for online retail. It is intended to adjust product rankings and recommendations using shopper behaviour, catalogue data and trading conditions such as inventory levels, promotions and new product launches.

Product discovery has become a central issue for online merchants as retailers try to balance shopper preferences with commercial priorities. That often means deciding which products appear first, how new items are introduced and how promotions are reflected across search and browsing journeys.

Merchandisers can set objectives and limits for the model, including product exclusions, campaign priorities, editorial placements and inventory requirements. The system then optimises within those parameters, rather than relying on manual product placement or fixed rules.

The model supports both first-time and returning visitors. Fast Simon said the aim is to improve conversion rates, increase average order value and lift revenue, while reducing repetitive merchandising tasks.

Zohar Gilad, Chief Executive Officer and Co-Founder of Fast Simon, said the challenge for merchants is extending merchandising judgement across a large number of customer interactions.

"Merchandisers know their brand, their customers, and the business goals they need to achieve. The challenge is applying that judgment across every product position and shopper journey," said Zohar Gilad, Chief Executive Officer and Co-Founder of Fast Simon.

He said the company's approach combines merchant oversight with automated optimisation and testing.

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