BigCommerce and Generative Engine Optimisation (GEO)
Shopify has been loud about its AI commerce positioning. Agentic Storefronts, ChatGPT integrations and Catalogue syndication. The marketing has been consistent and high-volume. If you are on BigCommerce, you may have noticed the noise and started wondering whether you are behind.
Shopify does have more developed AI platform integrations at this point, and that gap is real. But for most merchants, it is not the main constraint.
The brands that will struggle with AI discoverability usually have the same problem: poor product data. And that problem exists on every platform, including Shopify.
Please note: AI commerce is moving quickly. The platform capabilities described in this article reflect the position as of early 2026.
What Is Generative Engine Optimisation?
Generative engine optimisation (GEO) is the practice of structuring your brand, content, and product data so that AI tools — ChatGPT, Google AI Mode, Perplexity, Microsoft Copilot — surface and recommend your products when buyers use them to research purchases.
Unlike traditional search, where a buyer types a query and scans a results page, AI tools synthesise information and produce a single recommendation. If your products are not clearly represented in the data that those tools can access and interpret, you do not appear. There is no page two.
GEO matters now because AI-referred traffic and AI-attributed orders are both growing quickly. The buyers using these tools tend to be higher intent; they have already described what they want in some detail before they receive a recommendation.
What BigCommerce Actually Gives You
BigCommerce's architecture has several features that help with AI discoverability, even if the platform has not marketed them in those terms.
A robust, well-documented API
BigCommerce is API-first by design. Product data, catalogue structure, inventory, and pricing are all accessible via clean, structured endpoints. AI agents and third-party integrations can pull accurate, real-time data from a BC store without the data quality compromises that come from scraping a storefront. For merchants already using headless or composable architecture on BigCommerce, this advantage is even more pronounced.
Server-side rendering options
BigCommerce's Stencil framework renders pages server-side by default, which means AI crawlers can read your content directly. Headless implementations vary by front-end framework, but the platform itself does not impose a crawlability barrier.
Structured product data by default
BigCommerce's native catalogue supports product types, custom fields, variant options, and category taxonomy out of the box. The infrastructure for well-structured data exists — whether merchants have used it well is a separate question.
What BigCommerce cannot do is the same thing Shopify cannot do: decide how your products are titled, how your catalogue is organised, or how complete your product information is. The platform provides the structure. What sits inside it is your responsibility.
How AI Agents Decide What to Recommend
When someone asks ChatGPT for a product recommendation, the agent does not simply retrieve a list. It breaks the query into multiple sub-queries, runs them against search indexes and available data feeds, synthesises the results, and produces a recommendation based on what it can confidently interpret and verify.
A query like “waterproof walking boots for wide feet under £120" will generate sub-queries around product type, fit attributes, price range, and availability. The agent is looking for products it can specifically match to those criteria. If your product data says “outdoor boot" with a description focused on brand story rather than construction details, you are asking the agent to guess. It will not guess in your favour when a competitor's listing says “waterproof leather walking boot, wide fit, D width available, £89–£119."
Brand authority also plays a role. AI agents draw on reviews, press coverage, community signals, and third-party references to assess whether a brand is credible enough to recommend. But for most BigCommerce merchants in the £1m–£20m range, the more immediate problem is the product data, not the brand profile.
Where Most BigCommerce Merchants Fall Short
BigCommerce attracts merchants who want more control and flexibility than simpler platforms offer. That usually means more complex catalogues — more SKUs, more variants, more custom product configurations. Which means that, when they exist, data problems tend to be larger in scope.
Vague or marketing-led product titles
A title like “The Wanderer Boot" or “Pro Series Trainer" tells an AI agent very little. It cannot confidently match that title to a specific query. Most merchants need both: a display name for the storefront and a structured title and tags in the data layer.
Incomplete product descriptions
A description that converts a human browser is not the same thing as one that informs an AI agent. Human-facing copy emphasises feel, story, and aspiration. AI-facing data needs materials, dimensions, fit notes, use cases, and specific attributes. If your descriptions do not contain this information, AI agents cannot extract it. Many BigCommerce catalogues, particularly those built during a migration from a legacy platform, have the former and little of the latter.
Custom fields left empty or inconsistently used
BigCommerce supports extensive custom field configuration — a genuine advantage for structured data. In practice, many merchants set up custom fields during implementation and then populate them inconsistently, or not at all. An AI agent querying your product data via the API will see those empty fields. A competitor with complete custom field data will be easier to match to a specific query.
Variants modelled as separate products
If the same jacket in four colourways exists as four separate products in your catalogue, an AI agent will not automatically understand they are the same item with different options. It may surface one, miss the others, or treat them as competing products. BigCommerce supports robust variant modelling — grouped under a single parent product with clearly labelled options — but not all catalogues are built that way.
Taxonomy that is too broad
BigCommerce's category structure allows for granular taxonomy, but many merchants use broad top-level categories because it was quicker to build. For GEO purposes, the most specific product type that accurately describes the item is what matters. "Women's waterproof hiking boots" can be matched to a query. "Footwear" cannot.
The BigCommerce vs Shopify AI Question
We’ve come across BigCommerce merchants considering a move to Shopify, partly because of Shopify's AI commerce marketing. Here is what that move would and would not change.
Shopify has built direct API relationships with AI platforms including ChatGPT and Perplexity, and has invested in Agentic Storefronts infrastructure. Those are real capabilities, and they give Shopify a distribution advantage at the platform level. If Shopify's Catalog syndication becomes the dominant channel for AI product discovery, merchants outside that ecosystem will face a harder route to visibility.
What a move to Shopify would not change is the state of your product data. Vague titles, incomplete descriptions, broken variant structure, and inconsistent taxonomy migrate with the catalogue. Merchants who have replatformed, expecting AI visibility improvements and found they did not materialise, have, in most cases, moved a data problem from one platform to another.
If AI discoverability is the primary reason you are considering a replatform, start with a clear assessment of whether your current data quality is the constraint.
The Commercial Stakes Over the Next Three Years
AI-referred commerce is not a niche behaviour. Shopify's own reported data puts AI-referred traffic up roughly 9x and AI-attributed orders up 14x since January 2025. Those are Shopify's numbers and come with obvious commercial incentives, but the direction matches the wider market
For brands doing £1m–£20m, a growing share of new customer acquisition is likely to move through AI recommendation channels over the next three years. The brands visible in those recommendations will pull away. Paid and organic search will become more competitive as high-intent buyers migrate to AI-assisted discovery.
BigCommerce's platform-level AI integrations are less developed than Shopify's at this point. That gap may close; platform capabilities in this area are moving quickly. But it still matters when you evaluate platform choices.
A Practical Audit You Can Run Now
Before commissioning a full catalogue review, run this yourself in under an hour.
Open ChatGPT and ask: "What do you know about [your brand name]?" Note what it says, which products it mentions, and whether the information is accurate. If it is pulling from outdated pages or getting key details wrong, those are the pages to fix first.
Then pick three of your most important products and ask: "Based on this product page [paste URL or content], what information is missing that would help you recommend this product?" ChatGPT will tell you directly what it cannot confidently interpret.
Finally, check your product titles against a specific query. Would someone searching for what you sell use any of the words in your current title? If the answer is no for most of your catalogue, that is a scoping exercise, not a quick fix.
The Migration Problem
Many brands that moved to BigCommerce from a legacy platform (Magento, WooCommerce, or a bespoke build) carried their existing catalogue structure across because the migration was already a large enough project. Rebuilding the taxonomy, rewriting the descriptions, and restructuring the variants went onto the post-migration list and, in many cases, stayed there.
What they carried across was a catalogue built for a different context: product titles written for a previous search environment, descriptions written for human browsers, category structure built around internal logic rather than buyer intent. BigCommerce did not create those problems, but the migration did not fix them either.
Those brands are now on a capable platform with product data that will not perform well in AI discovery. The catalogue they migrated is the catalogue that will be queried.
If You Want a Structured View of Where You Stand
Strawberry's Clarity diagnostic examines the commercial and technical structure of an ecommerce operation before recommending changes. For BigCommerce merchants with questions about AI readiness, or who are weighing whether a platform move is warranted, that includes assessing catalogue architecture, data completeness, variant structure, and taxonomy against current requirements.
Clarity is a fixed-scope, paid engagement. It provides a clear picture of what is working, what is not, and the likely impact of addressing it.
FAQs
Does being on BigCommerce put me at a disadvantage for AI discoverability compared to Shopify?
Shopify has more developed direct integrations with AI platforms at this point — Catalog syndication, Agentic Storefronts, and direct API relationships with ChatGPT and Perplexity. But for most merchants, the more immediate constraint is product data quality, not platform.
What is generative engine optimisation and how does it differ from SEO?
Traditional SEO optimises for search engine rankings. GEO optimises for AI recommendation systems that synthesise information and produce a single answer rather than a list of links. Many SEO fundamentals carry over: clean technical structure, accurate data, and authoritative content. GEO places significantly more weight on the completeness and specificity of product data.
Should I move to Shopify for better AI discoverability?
Only if platform-level distribution is genuinely the constraint. If your product data is incomplete, inconsistently structured, or built for a different search environment, a replatform will not fix the visibility problem — it will move it.
My BigCommerce catalogue was migrated from another platform — is that a problem?
It can be. Migrations typically carry across the existing data structure without auditing it for current requirements.
What does fixing BigCommerce product data for GEO actually involve?
Typically, it starts with auditing product titles, descriptions, variant architecture, custom fields, and taxonomy against GEO requirements, then a prioritised programme of updates.
