THE SHIFT
AI-assisted shopping has raised the bar for product information.
AI is increasingly being asked to interpret buyer needs, compare products and answer detailed questions — before shoppers reach your site and through AI assistants once they do. That changes what product information needs to do: not just describe the product, but answer the questions buyers rely on to decide what to buy. Most product information was never built for that.
Product intelligence requires judgement, not just more data.
More product data doesn’t automatically make a product easier for AI to evaluate and recommend. What matters is knowing which information shapes the buying decision — and what the available specs, reviews and other sources actually support about the product.
AI needs product information that holds up across sources.
AI builds its view of a product from catalog data, product pages, customer reviews and other sources. When those sources conflict, AI has less confidence in what is true — and less basis to match the product to the buyer or recommend it.
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Canonica checks what those sources actually support, qualifies claims that go too far and makes limitations clear, giving AI a stronger basis to evaluate and recommend your products.
 WHAT YOU RECEIVE
Product Intelligence your team can use, own and build on.
INTELLIGENCE PACKAGES
Start with one product or your priority range.
Single Product
For one flagship, launch or high-value product — or the natural next step after a Stress Test.
Starter
5 products. For a focused range, launch collection or small group of products buyers commonly compare.
Core — Most popular
10 products. For a priority range where buyer fit, meaningful differences and trade-offs need to be clearer across the set.
Growth
20 products. For a broader range where the same decision-ready product information needs to be built consistently across more products.
Need a different scope?Â
Everything you need to know before you start.
Catalog enrichment typically adds, completes or standardises product data — specifications, materials, categories and other attributes. That work is important, but it is usually built around standard category fields, which improve completeness without necessarily capturing what meaningfully differentiates one product from another.
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Canonica builds the decision-useful intelligence standard product data often misses: how a product performs, who and what it suits, what differentiates it from alternatives, and which buyer needs it can credibly match.
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That gives AI more of the information it needs to move beyond identifying products to actually comparing and choosing between them.
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.
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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.
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.
Usually, the priority product URLs or SKUs and the best product information you already have — such as catalog fields, specifications, manuals, supplier material, reviews and FAQs. Canonica does the research and analysis from there.
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No store or system access is needed to begin. Internal exports or additional product information can strengthen the work where available.
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.
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.
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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.
Timing depends on the number of products, the category and how much evidence needs to be reviewed. Canonica confirms the delivery timeframe before work starts, so the scope and timing are clear upfront.
One product means one distinct product with the same core suitability, performance and buying considerations.
Standard colour, basic size and similar variants are included when they do not materially change the buying decision.
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If a variant changes things like performance, compatibility, fit, construction or other important buying considerations, it may need to be treated as a separate product.