THE PROBLEM
Your product can be the right choice and still lose the sale.
Search has always influenced which products shoppers see, but buyers still opened product pages, compared options and read reviews before deciding what to buy.
AI systems like ChatGPT now do much of that research and comparison within the conversation, interpreting detailed buying needs and selecting which products to put forward before the buyer clicks through. If AI leaves your product out, the buyer moves on before your site ever gets the chance to convert them.
Most catalogs aren’t built to support the buying decision.
Today’s catalogs can look comprehensive, but they’re often built around standard attributes, supplier data and brand claims. Product Intelligence adds the evidence-backed context AI needs to assess whether the product fits the buyer’s needs.
WHY CANONICA
Adding more product data is easy. Knowing what matters is hard.
The hard part isn’t filling more fields or writing more copy. It’s identifying the information that actually matters to the buying decision, what can be supported by evidence, and what genuinely sets your products apart from the alternatives.
Canonica has built a proprietary methodology for making those judgements and turning the result into evidence-backed, structured Product Intelligence your team can put to work.
Built for AI. Useful wherever your products need to be understood.
BEYOND THE CATALOG
The same evidence-backed intelligence that helps AI evaluate and compare your products gives your teams and systems a more consistent source to work from, wherever those products need to be understood or sold.
What comes next depends on what we find.
HOW IT WORKS
Start with one product. The free Stress Test identifies what needs attention and recommends what to do next.
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 specifications.
Product Intelligence adds the decision-critical context AI needs to support buying decisions — whether the product fits a particular buyer, use case or set of requirements.
It captures things like:
-
Suitability and primary use cases
-
Who the product is and isn’t right for
-
Performance in specific situations
-
Limitations and trade-offs
-
Compatibility and hard constraints
The information is linked to supporting evidence and structured so it can be used consistently across your catalog, product pages, feeds and other channels.
Canonica is designed 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 part of the problem. SEO helps your content rank in search. AEO and GEO focus on getting your content surfaced, used or cited in answers generated by search and AI systems.
Canonica works at the product decision layer. When AI evaluates a product, does enough information exist to judge who it suits, how it compares, where it performs well, what its limitations are and whether it fits the buyer’s needs?
There’s a focus difference too. SEO, AEO and GEO apply across almost every kind of business. Canonica focuses on Product Intelligence for high-consideration ecommerce, where fit, evidence and comparison can materially affect the buying decision.
The disciplines are complementary: SEO, AEO and GEO can help your products and content get found and surfaced. Product Intelligence gives AI better information to evaluate whether a product fits the buyer’s needs.
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. No one can control how every AI system ranks or recommends products. Be cautious of anyone who says they can.
We focus on what you can control: the information available about your product. Canonica builds clear, specific and evidence-backed Product Intelligence that gives AI the context it needs to support buying decisions — who the product suits, how it compares, where it performs well, its limitations and when it’s a good fit.
That gives AI a stronger basis to put your product forward over alternatives when it genuinely fits what the buyer is looking for.
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.
Buyers have traditionally filled in the gaps themselves by reading reviews, comparing products and pulling information from multiple sources.
AI now does more of that research and comparison on the buyer’s behalf. If important product knowledge is scattered, inconsistent or left implicit, AI has to reconstruct the decision from whatever information it can find.
Canonica identifies the decision-critical knowledge that matters and turns the supportable parts into clear, evidence-backed Product Intelligence AI can use to understand, compare and recommend your products when they fit the buyer’s needs.
