Built for AI. Valuable across everything you already do.
This isn't just a bet on AI shopping and search. The same intelligence AI needs to understand, compare and recommend your products also makes your product marketing and sales more differentiated, credible and effective.
Most catalogs display products. Fewer explain them. Almost none help AI choose them.
THE PROBLEM
For years, buyers compared products, read reviews, and decided for themselves. Now AI does that first, before anyone reaches your site.
A buyer asks ChatGPT: "I have reactive, acne-prone skin and wear makeup all day. I need a moisturiser that won't clog pores or pill under SPF. What should I buy?"
If your catalog and reviews only carry the basics — type, price, ingredients — AI can't see what the buyer actually asked for: reactive-skin suitability, non-comedogenic support, how it wears under makeup. So the recommendation goes to a product AI can understand and trust.
Your catalog has product data. AI commerce needs product intelligence.
Generic product data is thin, patchy, and sounds like the category average. Product intelligence is what makes your products specific, differentiated, and trusted.
WHY CANONICA
Adding more product information is easy. Knowing what matters is hard.
Your product data lists what a product is. Size, material, specs. What it rarely says is what it's good for, who it suits, and where it falls short. Those are the things people ask AI about, and your data says the least about them.
More copy won't fix it. Neither will more fields in your PIM. Both only help once you know what belongs in them, and that has to be specific, and backed by more than your own marketing.
That's our work: figuring out what's genuinely true about each product, proving it from specs and real reviews, and structuring it so AI can use it.
Product intelligence powers every touchpoint.
BEYOND THE CATALOG
Your catalog is just the start. Canonica's product intelligence is the raw material that feeds everywhere your products need to be understood and sold.
HOW IT WORKS
See the gap. Then build the intelligence.
Start with one product. The Stress Test shows the gap. Product Intelligence builds the layer across your top SKUs. Review Intelligence turns reviews into evidence AI can trust.
Everything you need to know before you start.
Product data tells AI (and shoppers) what a product is: its title, price, size, material, and basic specs. Product intelligence goes beyond basic catalog fields.
It captures things like:
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Suitability and primary use cases
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Who the product is (and isn’t) right for
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Key limitations and trade-offs
All of it is evidence-backed and structured so AI systems can actually trust and use it when matching products to specific buyer needs. In short: it’s the difference between AI knowing your product exists and AI understanding it well enough to confidently recommend it.
Canonica is deisgned for brands selling products people research before they buy, where fit, evidence, and comparison shape the decision. Most useful in specialised, high-consideration categories. Least useful for commodity products bought on price alone.
They solve a different problem. SEO gets you ranked in search. AEO gets you into featured answers. GEO gets you cited when AI writes its response. All three are about visibility: getting seen, surfaced, or referenced.
Canonica works one layer down. Being visible gets your product considered. It doesn't decide whether AI puts it forward once a buyer says what they need. That depends on whether your product data is specific and evidenced enough to match the request and be trusted.
There's a focus difference too. SEO, AEO, and GEO run across every kind of business, from software to local services. We do one thing: product intelligence for high-consideration ecommerce, where fit, evidence, and comparison decide the sale.
Those disciplines get AI to see you. Product intelligence gives it a reason to recommend you. They work best together.
No. AI-assisted shopping makes the problem impossible to ignore, but the same product intelligence helps your human buyers just as much. Clear, specific, evidence-backed product information is what lets a real shopper decide with confidence, and it sharpens everything else you do: product pages, filters, buying guides, marketplace listings, support conversations, and review strategy, while reducing wrong-fit purchases and returns.
The same missing detail that makes a product hard for AI to understand usually makes it harder for your customers to choose, and your own teams to sell.
No disruption to your systems, and no big project. There's no platform migration, no rebuild, and no IT involvement, nothing changes about how your store runs.
On your side, the work is straightforward: you share your priority products and source material, we build the intelligence, and your team puts the finished values to work where they belong, from product pages and feeds to buying guides and filters.
It's the kind of work your teams already do, not a new system to learn. You can start with a single product before going further.
No, and be cautious of anyone who says they can. AI recommendations are not deterministic. The answer can change depending on the platform, prompt, buyer context, available sources, retrieval behaviour, and how the system weighs evidence at that moment.
No one controls every AI platform, prompt, or buyer. We focus on what you do control: the quality of the product intelligence AI relies on to interpret and match your products. You can't control every answer. You can make sure you're not passed over because the available information is too generic to support a recommendation, too incomplete to act on, or too weak to trust.
It's early, but it's not hype, and the signal is already clear. Nearly 60% of consumers now use AI to help them shop, and AI influenced over $14 billion in online sales on Black Friday alone. Traffic from AI tools to retail sites grew close to 700% year over year over the 2025 holiday season.
These aren't low-intent browsers either. Shopify reports AI-referred sessions convert at nearly 50% higher rates than organic search, with 14% higher order values, because they arrive already deep in consideration.
This isn't a bet on a distant future. It's a fast-growing channel already sending higher-value buyers than organic search. The brands preparing their product data now are the ones AI will have something to work with as it scales.
No. And the bar is rising, not holding. Today AI recommends products. Increasingly, agents are moving closer to completing the purchase, assessing fit and acting on the buyer's behalf. Data that was good enough for a person to interpret isn't good enough for a machine making the call. Waiting doesn't hold your position. It just means more of those decisions happen without you in them.
Because it's been getting rescued. When a buyer lands on your page, they read between the lines, check the reviews, and fill in the gaps themselves.
That safety net is disappearing. When AI does the research and comparison, it works only with what your data explicitly says, with no human to interpret the thin bits. Data that reads fine to a person can leave AI with nothing specific to go on.
