AI is already deciding which products get considered.
THE SHIFT
We used to research and compare products ourselves, then decide. Now AI does the middle part. A buyer states what they want, and AI evaluates, compares, and returns a shortlist, deciding which products are in the running and which are left out.
This happens upstream, before your marketing or ads have any influence. And it runs on your product data. The clearer and better-evidenced that data, the more an AI can do with it. The thinner it is, the less you're in contention. Most catalogues weren't built for this. That's the gap Canonica closes.
Product intelligence is built through judgement, not just data.
OUR METHODOLOGY
Adding more attributes and tags is easy. The harder work is deciding what's true, what customers actually experience, what reviews confirm, and what to leave out. That gives AI clearer, evidence-backed information it can trust to compare and recommend your products.
GOVERNANCE
AI doesn't just believe what you say about your product. It checks.
So your product information has to hold up to that check. Governed intelligence makes sure it does: evidenced, bounded, and trusted enough for AI to stake a recommendation on.
AI cross-checks your claims against reviews, specs, third-party sources, and other signals. If the evidence does not support the claim, the claim is less likely to be trusted. So governed intelligence is not just specific. It's reconciled with the evidence AI can find.
The foundation your AI commerce runs on.
WHAT YOU RECEIVE
Everything your team needs to put product intelligence to work, built into IP your brand owns.
INTELLIGENCE PACKAGES
Start with the products that matter most. Scale up as you see the difference
It starts with a short call to scope the work and confirm fit. We take a limited number each month, and only ones we can help.
Need something more tailored?
Everything you need to know before you start.
No. The work is done from the product information and source material you share, no logins, no integrations, no IT involvement. Your systems stay yours.
Less than you might expect. A product name and URL are enough to start. Anything else you can share makes the work sharper: your product descriptions or page copy, spec sheets, review links or exports, and any manuals, ingredient lists, or sizing guides. It also helps to flag any claims you want handled carefully, and any known complaints, returns, or fit issues. The more you share, the deeper the intelligence goes, but we can begin with very little.
Enrichment tools add attributes at volume. Canonika adds judgement. Every value we assign is checked against evidence, scored for confidence, bounded so it can't overreach, and reconciled with what customers actually report.
Where the evidence is thin or conflicting, we say so rather than fill the gap. The result isn't more fields. It's product data an AI can rely on, and relying on it is exactly what decides whether your product gets matched to a buyer or passed over.
No. Canonica defines the product intelligence; your team or developer deploys it. Someone first has to work out what each product actually means, which attributes matter, what the evidence supports, and which queries it should answer. That's the hard part, and it's what we build. Putting the finished values into your pages, metafields, feeds, or filters stays with your team, and the Record shows exactly where each one goes.
It's built to be used across the teams that shape how a product is understood and sold. Your team can use it to sharpen product pages with clearer fit, suitability, and evidence; write stronger buying guides and comparisons; add better attributes to your PIM, feeds, and filters; brief copywriters, merchandisers, SEO, agencies, and support with consistent product logic; catch over-broad or unsupported claims before they cause returns or trust issues; and decide which products to build next.
Either works, but they do different jobs. The free Stress Test proves the gap on one product: it shows where your information falls short against real buyer questions. It's a useful first step if you want to see the problem before committing, but it only diagnoses.
It doesn't fix anything. The build is where the work happens, we construct the product intelligence that makes your products hold up when buyers and AI evaluate them. If you already know your information is too generic or inconsistent, there's little point proving a gap you can see. You can start the build on a single product, see the difference, and expand from there.
Brands with products where the buying decision isn't obvious, products customers compare, question, research, return, or ask AI to evaluate before buying. It's most useful where fit, suitability, use case, performance, compatibility, durability, limitations, reviews, or claims shape the decision: performance footwear, outdoor gear, clinical skincare, baby gear, home appliances, consumer electronics, technical apparel, and other high-consideration categories.
These are also your highest-margin, most-researched products, the ones where being passed over for information that's too generic doesn't mean a small loss. It means the sale going to a competitor an AI could stand behind instead. It's less useful for simple commodity products bought mostly on price, style, or availability. If that sounds like your catalog, the free Stress Test shows you the gap on a single product before you commit to anything.