How to Get ChatGPT and Perplexity to Index Your Shopify Store

If your Shopify store still relies on outdated SEO tactics, you’re probably missing out.

Search has changed. AI models don’t just look for keywords. They interpret meaning. If your content is not structured in a way that AI can understand, most likely you won’t show up, even if your product is a perfect match.

This guide walks you through exactly how to get your Shopify store recommended by large language models (LLMs) like ChatGPT and Perplexity.

Let’s start with the shift itself.

Consumer Behavior Has Changed

Customers, including you and me, don’t just “Google it” anymore. We’re asking ChatGPT and Perplexity, and get direct answers, summaries, cards, and conversations that look nothing like traditional search results.

Google Search Results vs AI Search Results

The problem is that most Shopify stores are completely unprepared for this shift. Not because they don’t have content, but because their content isn’t structured in a way AI can understand.

With this shift in consumer behaviour, visibility in 2025 isn’t just about where you rank on Google. It’s about whether your Shopify site is understood by LLMs like ChatGPT, Perplexity, and even Shopify’s own built-in search tools, Magic and Sidekick.

AI Discovery And Why SEO Alone Won’t Save You

While Google still dominates transactional queries, they’re no longer the only place discovery happens. ChatGPT now handles over a billion searches a week. Perplexity is quietly indexing eCommerce content, citing sources, and linking directly to PDPs.

OpenAI has introduced shopping features within ChatGPT, allowing users to receive personalized product recommendations complete with images, reviews, and direct purchase links. While transactions are currently completed on the merchant’s website, this integration marks a significant step toward a more seamless shopping experience within the ChatGPT interface. 

Moreover, Perplexity AI has launched its “Buy with Pro” assistant, enabling U.S.-based Pro subscribers to make one-click purchases directly within the platform. This includes integrations with payment services like PayPal and Venmo, allowing for a complete shopping journey, from discovery to checkout, without leaving the chat interface.

Shopify is leaning into this shift. 

Last month, I was at Editions.dev in Toronto, where I spoke with Shopify President Harley Finkelstein and saw firsthand how seriously they’re investing in AI. They rolled out platform-wide updates like semantic search, smart product recommendations, and an AI shopping assistant.

With Harley Finkelstein, President of Shopify, at Editions.dev in Toronto

Before AI, to get a website ranking, we’d follow certain steps.

The Old Model:

• Write an SEO blog post
  
• Optimize your product page title
  
• Hope for Google to reward the keyword
  
• Chase backlinks
  

With AI in the picture, the way we need to structure a website has changed.

The New Model:

• Describe what your product does in plain, structured language
  
• Contextualize it with use cases and buyer intent for relevance
  
• Connect related products, collections, and content to build meaning
  
• Feed LLMs the data and narrative they need to understand and recommend your brand
  

What Is Semantic SEO in eCommerce?

Semantic SEO in eCommerce involves optimizing a website and product descriptions to provide content that’s semantically relevant to user intent, rather than focusing only on keywords.

In old-school SEO, stuffing “men’s running shoes” five times into a paragraph would sometimes work. Google might not love it, but it understood what you were trying to do.

But LLMs don’t scan for keywords. They interpret meaning.

Think of Google as a librarian who catalogs pages based on known labels, while ChatGPT is a conversation partner trying to answer a question in plain language.

That’s essentially what semantic SEO is. It’s a mindset shift from “rank for the keyword” to “be relevant for the question.”

In a real-life scenario, this looks like:

User asks ChatGPT:
“What’s a good carry-on backpack that fits under the seat and holds a 15-inch laptop?”

The model pulls from its internal index to surface products that clearly meet that need.

Now ask yourself: Would your product page make the cut?

If your description says:

“Modern design. Spacious. High-quality materials.”

That’s noise. To a machine, it says nothing about the size, function, or user.

Now compare that to:

“This 22L travel backpack fits under the seat on most major airlines and holds laptops up to 15.6 inches. Designed for business travelers who need quick access to tech gear and essentials without bulky overhead bags.”

It’s not about keyword stuffing. It’s about teaching the model what your product is, who it’s for, and how it fits into the buyer’s life.

Why Most Product Pages Don’t Show Up?

Most Shopify product pages are written for people, which is a great start.
But they’re often:

• Too short to give context
• Loaded with vague claims like “durable” or “premium quality”
• Lacking real-world use cases or comparison points
• Disconnected from related content like buyer guides or FAQs

This isn’t just bad for SEO. It leaves AI models guessing.

When an LLM doesn’t understand your product with certainty, it leaves you out.

So What Should You Be Doing?

I was recently shopping for a ceramic pan and used Perplexity, ChatGPT, and Google to compare options.

Take this ceramic frying pan from Taramontina. Likely a great product, but it’s missing what matters:

• Clear use case language
• Structured data
• A sense of who it’s for and why it’s different

Now imagine that same product page rewritten with semantic SEO in mind:

• A descriptive, keyword-rich title
• A short intro paragraph explaining what it is, who it’s for, and when it’s best used
• Transformation bullets that show outcomes, not just features
• A Q&A block that anticipates real buyer concerns
• Properly implemented schema (Product, Offer, Review, FAQ)

What Flat PDPs Get Wrong (and How to Fix Them)

PDPs like the Taramontina example were nowhere in my AI recommendations. To get such a page to be recommended, I would start with semantic language that’s geared towards user intent:

Flat Description ProblemSemantic SEO Solution

Too short to establish context	Intro paragraph that describes what it is, who it’s for, and when it’s used
Uses vague adjectives like “amazing,” “stylish,” or “durable”	Titles that include product type, use case, and key features
Lacks use cases or comparison points	Bullets that show transformation or outcome (e.g., “From scorched eggs to effortless cleanup”)
Disconnected from supporting content (e.g., guides or FAQs)	Q&A section that addresses real buyer concerns
Written only for humans, not machines	Comparative copy and structured data (Product, Offer, Review schema)

Semantically Optimized vs Non-Optimized PDP

The takeaway is that LLMs don’t care how fancy your brand voice is. They care whether they can understand what you’re offering, and confidently surface it when someone asks a real question that your product solves.

In other words, if the correct product description and structure are not baked into your PDPs, you’re not even in the running.

Smarter Product Content with Shero’s Verbalic AI

But how do you update product or collection page descriptions in bulk? Writing SEO-friendly content for thousands of products is no easy task. Verbalic, a tool built by our team in Finland, solves this by transforming disorganized product data into helpful, clear, and AI-optimized descriptions.

Verbalic rewrites product and collection pages using semantic SEO logic. That means content is easier to understand for search engines and more likely to be surfaced by AI tools like ChatGPT, Perplexity, and Google’s AI Overviews.

Here’s a quick before-and-after from F-Musiikki, a leading music retailer in Finland:

PDP before and after Verbalic AI

Our team migrated more than 15,000 products from Magento to Shopify Plus, and Verbalic helped restructure all content to improve clarity, consistency, and conversion. It now works across product pages, collections, and support content, building AI visibility while saving internal teams time and stress.

If your product descriptions or pages still read like boilerplate, this is your sign to update them.

Want a demo or case study? Reach out to our team →

How To Structure Content for LLM Visibility

By now, it’s clear that optimizing product descriptions, either manually or with Verbalic, alone won’t cut it. What we need to create is an interconnected network-level structure.

If your store lacks those connections, it doesn’t matter how polished your copy is. LLMs won’t have enough to work with.

Creating Connections That AI Understands

As mentioned above, AI doesn’t see each page individually. That means your content needs to act like a web of meaning, not a bunch of standalone pages.

Here’s how I recommend you start right away:

• Collection intros – without a few visible sentences explaining who the collection is for and what problems it solves, LLMs have no context to understand the category.
• Blog-to-product linking – if your buyer guides, FAQs, or comparisons don’t link directly to specific products or collections, they’re not helping build meaning across your site.
• Product-to-blog linking – when PDPs don’t connect to supporting blog content, you lose an opportunity to reinforce trust and relevance with both users and machines.
• FAQs with schema – if your product or collection pages lack real customer questions marked up with structured data, LLMs may skip critical information buyers care about.
• Structured reviews – displaying reviews without schema means AI tools can’t parse sentiment, surface reputation, or summarize what customers are really saying.

For example, this website could greatly benefit from adding collection intros:

Mistakes You Should Avoid

These common elements might look fine visually, but can harm your AI discoverability:

• Tabs or accordions that hide key copy – when product specs, sizing, or materials aren’t visible on page load, they may be skipped by both search engines and LLMs.
• Collection pages with no intro text or internal links – without visible context or connections, the category offers no clear signal about what it represents or who it serves.
• Orphaned blog posts – without links to products or collections, blog content sits in isolation and adds no semantic value to your overall site structure.
• Duplicate product descriptions from suppliers – reusing generic content found on dozens of other sites gives LLMs no reason to trust or prioritize your version.
  

Again, think of your website as a knowledge source, not a catalog.
You’re not just presenting products, you’re explaining them in context.

When you structure your content like a conversation, LLMs can respond in kind:

“Looking for a ceramic pan that works on induction stoves? Brand X has one that’s dishwasher-safe, oven-safe, and highly rated for even heat distribution.”

That’s what it sounds like when your site teaches the model exactly what it needs to know.

What an LLM-Ready Product Page Actually Looks Like

Let’s stop talking theory and start getting tactical.

Since we are trying to be included in AI-powered answers, whether someone’s asking ChatGPT for “the best carry-on backpack for tech gear” or using Perplexity to research “eco-friendly cookware for new parents”, your product pages need to do more than sell. They need to teach.

Why? That’s because LLMs aren’t just scanning for headlines or counting keywords. They’re building an internal narrative of what your product is, how it’s used, who it’s for, and how it compares to alternatives.

So, let’s walk through what a well-structured, LLM-ready product page looks like and what most Shopify stores are still getting wrong.

The Anatomy of an LLM-Friendly PDP

Let’s continue using my example from above with the ceramic pan.

Title (Keep it under 70 characters)

Example:
10” Ceramic Frying Pan – Non-Toxic, Dishwasher-Safe, No PFAS

This format defines the product type, core features, and avoids brand small talk. LLMs look for clarity over cleverness.

Intro Paragraph

Add an intro paragraph to your products. For example:

“This 10-inch non-toxic frying pan is made from ceramic with zero PFAS, PFOA, or Teflon. Ideal for home cooks, families, and anyone trying to reduce chemical exposure. Compatible with gas, electric, and induction stovetops. Oven-safe up to 500°F.”

It gives LLMs everything they need upfront: product type, materials, benefits, use cases, and compatibility.

Transformation Bullets

Add a few transformation bullets. Do not list just product features. List outcomes instead.

• From harsh chemicals to chemical-free cooking
• From uneven heat to consistent, golden-brown sears
• From cluttered cabinets to one do-it-all everyday pan

These reinforce value through the lens of before-and-after outcomes, which LLMs are trained to summarize and recommend.

Q&A Block

Use schema to mark up answers to real questions people ask:

Is this pan dishwasher safe?
Yes, but hand washing is recommended to preserve the coating.

Does it work on induction?
Yes, it has a ferromagnetic base for induction cooktops.

Can I use it in the oven?
Safe for oven use up to 500°F.

This format mirrors how LLMs structure answers. You’re giving them quotable, trusted content to pull from.

Structured Data (Use JSON-LD or a Shopify app)

• Product schema – name, brand, description, image, SKU, GTIN/UPC
• Offer schema – price, availability, condition
• Review schema – sentiment, ratings
• FAQ schema – every Q&A block should be marked up

This isn’t just about SEO. Schema tells LLMs what every piece of content actually means.

Internal Linking

• Link back to the “Non-Toxic Cookware” collection
• Add a CTA to a related blog post like “Why We Avoid Teflon (and You Should Too)”

This further helps LLMs connect (the dots) product pages to broader topics and reinforces the product’s relevance in your overall catalog.

Common Mistakes to Avoid

Some of the most common mistakes I see when it comes to PDPs are:

• Empty or Generic Titles – names like “Eco Pan” or “The Everyday Fryer” might sound branded, but they fail the clarity test.
• Overreliance on Tabs or Accordions – important content hidden inside UI elements may be missed entirely by AI tools.
• No Use Case Language – skip being generic. Phrases like “Great for busy parents” or “Ideal for small kitchens” help models connect products to real-world needs.
• Unstructured Reviews – LLMs won’t summarize reviews unless they’re marked up with schema that makes sentiment, rating, and themes machine-readable.
• Overdesigned, Underwritten Pages – design isn’t enough. Words still drive understanding. If you don’t say the right things in the right format, your product won’t surface.

When optimizing for LLM visibility, the question is not “Did we write enough copy?”
It’s: “Did we say the right things, in the right way, so a machine can explain our product when we’re not in the room?”

How to Connect Collections, Blogs, and Reviews into a Semantic Website

Getting product pages (PDPs) right is important. But they’re only part of the equation. If all you have optimized are your product pages, you’re giving AI tools an incomplete picture.

LLMs do not evaluate your site page by page. They look for patterns, relationships, and consistency across your entire catalog. They are mapping out how your content connects, what belongs together, what reinforces meaning, and how well your site explains itself without needing a second guess.

Here’s how to fill in the blanks:

Add Context to Collection Pages

Most collection pages are overlooked. Many Shopify themes show only a grid of products with no intro, no explanation, and no connections. That means there is no signal for who the products are for, what problem they solve, or how they relate to the rest of your site.

A few clear sentences at the top of each collection page can solve this. Describe the use case. Mention the ideal customer. Link to a relevant blog post. These small details give AI models the context they need to understand what the category means, not just what it contains.

Make Your Blog Content Work For You

Your blog should not sit on an island. If your posts do not connect back to your catalog, they are unlikely to help with AI visibility.

Instead of gift guides and thought pieces with no internal links, focus on content that answers real questions and ties into your products. A post like “What Cookware Is Safe for Induction?” should link to your induction compatible pans. A guide to long-distance running shoes should connect back to the category and at least one product.

Add internal links with natural phrasing, alt text that reinforces context, and schema that defines article type, EEAT, and breadcrumbs. These are not SEO tricks. They are clarity cues that help LLMs learn how your site is structured and what your products help solve.

Let Reviews Do More Than Validate

Customer reviews are not just social proof. They are a goldmine of natural language. That is the same language LLMs use to identify patterns and summarize product strengths.

To make use of them, do not just display your reviews. Mark them up with schema that highlights sentiment, star rating, and review themes. Aim for a volume of reviews that gives your product credibility in the eyes of AI systems, not just human shoppers.

You can also pull snippets from reviews into your product content. Phrases like “Heats evenly and wipes clean every time” or “Supportive after hours on your feet” reflect real-life use and feed the model more detail than a generic benefit ever could.

Create a Web of Internal Links

Think of your store as a network, not a collection of isolated pages. The more your content reinforces itself across pages, the easier it is for machines to understand your business.

This does not mean flooding your site with links. It means making intentional and descriptive connections that explain how your products, collections, and content all support the same buyer journey.

Examples of smart internal linking:

• From product pages to relevant buyer guides or blog posts
• From blog posts to matching products and collections
• From collections to top performing products and FAQs

And always use anchor text that reflects meaning. A link that says “Why we avoid Teflon” tells the model something. A link that says “Click here” does not.

That’s semantic SEO in practice.

Technical SEO for AI Search & LLM Indexing (in 7 steps)

So far, we’ve covered the theoretical aspects of preparing a site to be discovered and indexed by LLMs.

The first step is allowing LLMs to crawl your website.

1. Let AI Crawlers Access Your Site

AI models rely on dedicated crawlers to scan and index product content. OpenAI uses oai-searchbot. If your site blocks this bot, you won’t be included in ChatGPT shopping results.

In your robots.txt file, add:

User-agent: oai-searchbot
Allow: /

For broader visibility, you can also allow other key bots:

User-agent: ChatGPT-User
Allow: /
User-agent: GPTBot
Allow: /
User-agent: ClaudeBot
Allow: /
User-agent: PerplexityBot
Allow: /
User-agent: Google-Extended
Allow: /
User-agent: CCBot
Allow: /

This gives AI crawlers permission to access your content.

2. Create an llms.txt File to Guide AI

Think of this as a sitemap just for LLMs. It tells them which pages best represent your brand. You can take look at the LLMs.txt file we use here.

To do so, create a file called llms.txt and include:

• Your brand name
• A short brand description
• Links to your most useful pages: top products, core categories, and strong blog posts

We are not an eCommerce site, but this is what your llms.txt file would look like:

Shero Commerce Cookware ;)
Description: Non-toxic, PFAS-free cookware trusted by 100,000+ families.

Pages:
https://sherocommerce.com/products/10-inch-ceramic-pan
https://sherocommerce.com/collections/non-toxic-cookware
https://sherocommerce.com/blog/is-teflon-safe

Upload the file to your root domain: yoursite.com/llms.txt.

To make this even easier for you, I have created this Shopify Store LLMs.txt File. You can copy and edit to match your brand, and then uploadit on your website.

3. Add and Maintain Structured Data

Use schema markup to translate your content into machine-readable formats. This isn’t just for Google anymore. LLMs also rely on structured data to verify and summarize product details.

Prioritize these schema types:

• Product: Name, brand, SKU, GTIN, material, description
• Offer: Price, availability, condition
• Review: Star rating, sentiment, review count
• FAQPage: For Q&A sections under PDPs
• Article: For blog posts that answer questions

You can implement this with top-rated Shopify apps like JSON-LD for SEO, Schema Plus, or Shopify’s native metafields.

4. Keep Product Data Fresh

LLMs favor sources with accurate pricing and real-time inventory. If your product page shows something as “in stock” but it isn’t, that hurts trust.

Set up:

• Real-time updates via Shopify webhooks
• Automated lastmod updates in your XML sitemap
• Error-proof PDP templates that always reflect current status

If your data is stale and your competitor’s isn’t, they’ll be the ones shown.

5. Track AI-Driven Traffic

You can’t optimize what you can’t measure.

Use UTM parameters to track traffic from LLMs like ChatGPT, Perplexity, and Claude:

?utm_source=chatgpt
?utm_source=perplexity
?utm_source=openai&utm_medium=llm
?utm_source=claude

Monitor these in GA4 or your analytics tool to understand where you’re gaining traction. To tsee LLM traffic, go to Reports → Acquisition → Traffic acquisition and filter by session source. You can also check Engagement → Landing pages for visits without a clear source, or build a custom exploration to isolate traffic from AI domains.

How to find AI Traffic in GA4

6. Apply to OpenAI’s Product Discovery Program

OpenAI is testing a new way to feature products in ChatGPT results through a beta merchant program.

As of May 2025, OpenAI has launched a beta product discovery program. If your product pages are structured, specific, and clear, you can apply here:
https://openai.com/chatgpt/search-product-discovery

This is early-access territory. If accepted, your products could show up in AI shopping cards, which is a big head start over competitors.

7. Understand What Gets You Picked

It’s not just about being indexed. It’s about being understood well enough to be recommended.

LLMs like ChatGPT are trained to give direct answers. That means they’ll only mention your product if it fits the question with clarity, confidence, and relevance.

To recap, what the model is scanning for:

• Does your product solve the exact need expressed in the question?
• Do you name the use case, not just the features?
• Is your content connected to blog posts, FAQs, and reviews that support it?

Ready for the final section?

Shero AI Indexing Mega Checklist (June 2025)

Here is a 28-step checklist you can use to make sure your Shopify site has everything LLMs look for in order to recommend your site in answers.

You can also access and download the table by visiting this link AI Indexing Mega Checklist.

No.What to DoWhy It MattersPhaseKPI / Action Check✅ Done?

1	Shift your mindset from keyword ranking to relevance and clarity	AI discovery focuses on meaning and context, not keywords or backlinks	Mindset	Familiarize yourself with Semantic SEO	☐
2	Write clear product titles under 70 characters with product type, use case, and benefit	Helps tools like ChatGPT and Perplexity rewrite titles to match real buyer questions	Content	Title includes product type and benefit	☐
3	Use a first paragraph that explains what the product is, who it is for, and when it is used	Provides context that both customers and AI models understand	Content	Intro paragraph defines audience and use case	☐
4	Include three transformation bullets that describe outcomes rather than features	Aligns with how language models summarize and recommend products	Content	3 bullets, under 10 words, outcome-focused	☐
5	Add a questions and answers section with schema to address buyer objections	These sections are often quoted directly by ChatGPT and other tools	Content	Q&A section present and marked up with schema	☐
6	Build trust and social proof for recommendation engines	Images are clear, large, and with context	Content	30+ reviews per product	☐
7	Make sure structured data is present and valid	Structured data is what helps AI understand and explain your products	Technical	Structured data present and valid	☐
8	Allow OAI SearchBot to crawl your site using robots.txt	Without access, your site will not be indexed or recommended by ChatGPT	Technical	OAI bot not blocked	☐
9	Create and upload a llms.txt file listing your brand information and top pages	Acts like a sitemap for AI models, helping them prioritize the right content	Technical	llms.txt file published to root	☐
10	Ensure canonical and hreflang tags are correctly implemented on every page	Prevents duplication and helps with proper geographic and language targeting	Technical	Canonical and hreflang tags verified	☐
11	Use high-quality product images at least 800 pixels wide with no overlay text	Ensures pricing and availability are always accurate in AI tools	Technical	Inventory sync in place	☐
12	Automatically refresh your sitemap when inventory or price changes	Helps LLMs and search engines trust the freshness and accuracy of your site	Technical	Sitemap includes accurate lastmod	☐
13	Use high quality product images at least 800 pixels wide with no overlay text	Clean visuals improve AI understanding and presentation in product carousels	Content	At least one content hub live	☐
14	Add short introduction paragraphs to every collection page	Gives AI the context it needs to understand product groupings	Content	Intro added to each collection	☐
15	Link blog posts to product and collection pages using descriptive anchor text	Strengthens internal linking and reinforces product relevance	Content	Descriptive internal links used	☐
16	Build content hubs around real buyer needs and themes (e.g. non-toxic cookware or shoes for travel)	Connects your store to intent and helps establish topical authority	Content	At least one content hub is live	☐
17	Apply to the OpenAI Product Discovery Program	Early adoption increases your odds of appearing in ChatGPT’s shopping features	Visibility	Applied to OpenAI program	☐
18	Prepare to submit a direct product feed when OpenAI opens it to merchants	This will give you better control over accuracy and how products are displayed	Technical	Feed prep complete	☐
19	Track ChatGPT traffic in GA4 using UTM parameters like ?utm_source=chatgpt ?utm_source=perplexity	Helps you understand how much traffic and revenue are coming from ChatGPT and other AI tools	Analytics	Monitor traffic using UTMs	☐
20	Monitor traffic from Perplexity, Claude, and internal site search	Shows what content is working and where customers are still getting stuck	Analytics	Tracking log referrer data	☐
21	Review product, collection, and blog pages quarterly to identify content gaps	Keeps your site optimized for evolving AI expectations	Maintenance	Quarterly review logged	☐
22	Use image alt text and ItemList schema to clarify content groupings	Improves how AI tools understand and organize related products	Technical	Alt text and ItemList schema present	☐
23	Include GTIN in your Product structured data	GTIN helps ChatGPT and other AI tools correctly identify, match, and display your products	Technical	GTIN included in product schema	☐
24	Keep each transformation bullet under 10 words	Short bullets improve readability and align with how AI generates concise summaries	Content	Each bullet ≤10 words	☐
25	Brand is visible on review platforms and forums	Signals content freshness to both traditional search engines and AI models like ChatGPT and Perplexity	Technical	Sitemap includes lastmod for PDPs	☐
26	Align product language with actual buyer search intent	Helps language models map your product to the real-world problems shoppers are trying to solve	Content	Product copy reflects user queries	☐
27	Engage in forums and optimize third party review platforms for visibility	External visibility and backlinks boost authority and increase inclusion in AI-generated recommendations	Visibility	Brand visible on review platforms and forums	☐
28	Run a quarterly audit to ensure your Shopify store is AI optimized	Helps catch semantic gaps, outdated schema, or content misalignment before they impact performance	Maintenance	Audit completed last 90 days	☐

You’ve made it this far, which means you already understand what most Shopify brands don’t: that SEO in 2025 isn’t just about optimizing for Google, it’s about being understood, trusted, and recommended by AI systems that are quickly becoming the default way people discover products.

Final Thoughts

Let’s zoom out.

Five years ago, your brand’s homepage was the first impression. Today, your homepage might be a product card in ChatGPT.

Discovery has moved upstream.

You can’t afford to leave it to chance. The brands that win in this new landscape will be the ones who:

• Build product pages with intent and clarity
  
• Connect content across their site in meaningful ways
  
• Invest in structure, not just aesthetics
  
• Help LLMs help their customers
  

Because here’s the truth:

AI isn’t replacing search. It’s becoming search.

And if your store can’t be read, interpreted, and recommended, you’re invisible.

But if it can? You’re not just showing up. You’re winning.

Need help getting your Shopify store ready for AI-first discovery?
Let’s talk!

Related Resources for Further Optimization

• Understand the broader AI SEO landscape → AI SEO for Ecommerce
• Top Shopify themes for rich data & structured SEO → Top 15 Shopify Themes for SEO in 2025
• Moving to Shopify or Shopify Plus? → Migrate to Shopify — Everything You Need to Know
• Need expert help with AI SEO or technical readiness? → Shopify SEO & Data Migration Audit
• Focus on speed & site performance → Shopify Performance Optimization
• For fully optimized Shopify Plus implementations → Shopify Plus Development Services