In recent years, every product team has asked some version of the same question:
Can’t we connect a general-purpose AI model to our product catalog and build the shopping experience ourselves?
It’s a reasonable assumption. Today’s AI models can recognize images, summarize documents, answer questions, and generate remarkably human responses. Commerce feels like yet another application just waiting to be built.
The reality, however, is more nuanced. General-purpose AI models can do commerce reasonably well. The problem is that “reasonably well” isn’t good enough to build a great shopping experience. When a shopper is looking for the right product among millions of constantly changing options, small differences in accuracy and relevance matter.
To respond to commerce’s need for its own AI model, we built our own: the Shoppable Intelligence Model, or SIM.
Why commerce is a different AI problem
While general-purpose AI models understand and process information, shopping requires AI that can connect products, content, and shopper intent in real time. Specifically, a useful commerce experience has to connect three things simultaneously: the content capturing someone’s attention, the products related to that content, and the intent behind that entire moment.
Imagine someone watching a home renovation video. A general-purpose model may recognize that it contains a sofa. Easy. A commerce model, on the other hand, can understand what kind of sofa it is, including its style, material, color, and other product attributes, then use those details to find relevant products that are actually available to buy.
These recommendations depend on much more than visual recognition. Retail catalogs are regularly rife with inconsistent descriptions, discontinued listings, incomplete metadata, and constantly changing pricing. Connecting an AI model to that data doesn’t automatically produce a great shopping experience; in fact, it could actually make it worse.
How SIM delivers better shopping experiences
Overall, commerce AI succeeds or fails on one deceptively simple task: returning relevant product recommendations. A recommendation might match visually but be the wrong version. At other times, it might simply be out of stock, or there may be a nearly identical product that’s actually available that gets overlooked. Those small differences determine whether someone keeps shopping with the tool or gives up.
We trained SIM around these commerce-specific retrieval challenges. Against leading public models, including OpenAI’s CLIP and Google’s SigLIP2, it delivers 25–50% higher retrieval accuracy across leading product discovery benchmarks, with even larger gains on public fashion datasets.
These figures are the result of SIM’s combination of commerce-native training with a live product graph, as well as its mobilization of continuous feedback from actual shopping behavior. Every training decision is optimized around shopping outcomes rather than broad content recognition, and every deployment helps improve the model using real commerce signals.
Thanks to this design, SIM is the basis of Shoppable Intelligence: the ability to understand products, content, and shopping intent together.
What Shoppable Intelligence makes possible
SIM is already leading to better commerce experiences. Across Shopsense deployments, improvement in precision has produced a 24.5% increase in shopper click-through rate, without customers changing their existing integrations.
Shoppable Intelligence also powers a growing range of commerce applications across four core areas:
- Data: Build, clean, and enrich product catalogs so they can support more accurate commerce experiences
- Experience: Turn content into shoppable experiences and power self-driving storefronts
- Discovery: Help shoppers find relevant and similar products across large catalogs
- Monetization: Create new revenue opportunities through commerce advertising
All of these applications depend on the same underlying capability: connecting products, content, and shopping intent in real time, all powered by SIM.
Of course, general-purpose AI will continue to improve. Commerce, however, has always depended on specialized infrastructure. We believe AI is following the same path.
SIM was built for that future. If you’re looking to launch commerce products, create new revenue streams, and build better customer experiences, reach out today for a demo.
