AI is already shaping which products make the shortlist.
THE SHIFT
Buyers still research and compare products themselves, but AI now does more of that work within the conversation. A buyer says what they need, and AI can narrow the options before they ever reach a product page.
To support those buying decisions, AI needs reliable information about who a product suits, where it performs well, how it compares, and the limitations or trade-offs that could affect the choice. Most catalogs weren’t built to provide that level of decision-critical context. That’s the gap Canonica closes.
OUR METHODOLOGY
Product intelligence is built through judgement, not just data.
Adding more attributes and tags is easy. The harder work is knowing what actually matters to the buying decision, what the evidence supports, and what to leave out. That gives AI better information to compare and recommend your products.
EVIDENCE
AI needs product information that holds up across sources.
AI can compare what your brand says with catalog data, customer reviews and other available sources. When that information conflicts, it becomes harder to judge how the product performs and whether it fits the buyer’s needs.
Canonica builds Product Intelligence from what the evidence supports, with conflicts, limitations and uncertainty made clear. That gives AI more reliable information to use when comparing and recommending your products.
WHAT YOU RECEIVE
A Product Intelligence asset your brand owns, with everything you need to understand and put it to work.
INTELLIGENCE PACKAGES
Start with the products that matter most. Scale up as you see the difference.
It starts with a short call to confirm fit and scope the work. Product Intelligence engagements typically range from AUD $6,500–$20,000.
Single Product
For a flagship, launch or high-value product, or a first paid build after the Stress Test.
Starter
5 products. A focused priority range, launch collection or small set of products buyers frequently compare.
Core — Most popular
10 products. A priority collection where meaningful differences in fit, use case, performance and trade-offs need to be clearer.
Growth
20 products. A broader range where consistent Product Intelligence is needed across more products and more buyer decisions.
Need a different scope?
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.
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.
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.
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.
The first place to use it is in your product catalog — the structured data layer AI systems rely on to identify, compare and reason about products.
Canonica builds the decision intelligence your existing product data was never designed to capture — who a product suits, what it is best for, how it performs, where its limitations are, and how it differs from alternatives. The parts that belong in structured product data can then be carried into the catalog.
From there, the same intelligence can be reused across product pages, feeds, filters, comparison tools, buying guides, merchandising, support and AI shopping experiences. That gives your teams a consistent basis for representing the product instead of reinterpreting it differently in every channel.
Every engagement also includes a Deployment Guide showing which intelligence should be used where and how it should be expressed.
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.
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.
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.
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.
Yes. Single-product engagements are available for flagship, launch or high-value products. Standalone builds start from AUD $3,000. If we’ve already completed a Product IQ Stress Test for that product, the paid build can start from AUD $1,750 where diagnostic work can be reused.
A product includes its normal size, colour and other decision-identical variants. If a variant materially changes how the product performs, fits, works or who it suits, we’ll confirm whether it needs separate assessment when scoping the engagement.