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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.
"Commerce-Native AI combines human expression with machine intelligence. Merchants set the direction, the model continuously optimizes, personalizes, and a simple, controlled A/B test shows how much difference it makes to their business," said Gilad.
Retail focus
Fast Simon described the model as purpose-built for eCommerce rather than adapted from more general artificial intelligence systems. Its approach links three factors: shopper intent, merchant objectives and commerce conditions.
In practice, the system is intended to react as customers browse, scroll and interact with products on a site. Retailers also retain direct control over what Fast Simon called guardrails, which shape how the model makes decisions.
Functions outlined by Fast Simon include commerce-aware optimisation, merchandising-aware personalisation, continuous learning from shopper interactions and visibility into the signals affecting product ranking. Retailers can also compare the new model with existing merchandising methods through controlled A/B testing.
Shopify base
The launch targets global brands operating on Shopify, where competition over conversion and basket size remains intense. Product discovery tools have become more important as retailers look for ways to present large catalogues more effectively, especially during promotional periods and major seasonal campaigns.
Fast Simon said the new model is available immediately. The announcement adds to the company's broader range of eCommerce merchandising and product discovery products, including tools for cross-border merchandising within Shopify Markets and Creative Studio.
Its platform is used by thousands of retailers on Shopify and focuses on search, merchandising, personalisation and shopping agents.