Built for AI. Valuable across everything you already do.
This isn't just a bet on AI commerce. 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 the SKUs that drive revenue. Review Intelligence turns reviews into evidence AI can trust.
Everything you need to know before you start.
When a buyer asks an AI what to buy, the AI reads your product data, reviews, and other trusted sources, then decides which products to put forward. Canonika makes sure your products give it something solid to work with: clear, specific, evidence-backed information that AI can understand, match to a buyer's need, and trust enough to recommend. We build that intelligence; your team or developer puts it in place.
No, and it's an important distinction. SEO gets your page to rank: the right keywords, links, and structure so search engines list you. It does nothing to help an AI understand whether your product actually fits what a buyer asked for.
That's a different job, and your SEO spend was never built to do it. A brand with flawless SEO can still be passed over the moment an AI looks for evidence your product suits the need and finds nothing specific enough to go on. This is the layer SEO doesn't touch: the product intelligence AI reads before it decides what to recommend.
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.
Built 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.
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.