The Complete Guide to AI SEO for Shopify (2026)

Last month, AI search drove 58% of monthly revenue at a Shopify jewelry store we measure — up from effectively 0% twelve months earlier. GA4 labeled most of it “Unassigned.”

That one sentence is why we wrote this guide.

In 2026, AI search is a real channel — not a future trend. Roughly 30% of Gen Z buyers research purchases with ChatGPT before Google. AI Overviews now appear on about 45% of Google result pages. Perplexity, Claude, Gemini, Meta AI — every major AI platform has a citable search interface, and every one of them is sending traffic to ecommerce stores. Most of it is invisible to your current analytics.

This guide is the 2026 playbook for Shopify operators. Nine chapters. Each one has a deep-dive companion article. By the end, you’ll know:

What AI SEO is, and how it actually differs from traditional SEO
Which AI engines matter, and how each one cites differently
The seven AEO levers that get a Shopify store cited
Which schema types AI engines actually read
How to write an llms.txt that AI crawlers will respect
Why comparison pages are the highest-ROI content type in 2026
The five patterns ChatGPT specifically rewards
How to measure AI search revenue (and why your current tool can’t)
The 8-week starter plan to put it all together

This is written for Shopify D2C operators between $10K and $500K monthly revenue. You already have GSC and GA4 connected, you’ve heard “AI search” on a few podcasts, and you want to know what to actually do.

If GSC and GA4 aren’t connected yet, start with the Shopify SEO starter first. If you run an enterprise Shopify Plus store with a full SEO team, you’ll move through Chapters 3, 4, and 8 faster than average. Skim those.

Let’s get into it.

What Is AI SEO?

If you’re confused by the alphabet soup — AI SEO, AEO, GEO, LLMO — you’re not alone. They overlap heavily, and practitioners use them interchangeably. Here’s the cleanest way to think about it:

AI SEO is the umbrella. Anything you do to optimize for AI-driven search results — whether that’s a chatbot, an answer engine, or an AI Overview — falls under this term.
AEO (Answer Engine Optimization) is the tactical sub-discipline. The concrete levers: schema markup, citable snippets, FAQ structure, comparison pages, quote injection.
GEO (Generative Engine Optimization) is the same idea framed specifically for generative AI like ChatGPT, Claude, and Perplexity rather than for traditional AI Overviews.
LLMO (Large Language Model Optimization) is the lowest-level technique: structure your content so the LLM picks you over a competitor when it generates an answer.

TL;DR — AI SEO is the umbrella. AEO / GEO / LLMO are tactical names that all roughly mean “make your content easy for an AI to extract.” Most people just say AI SEO. We use the same shorthand throughout this guide and call out the sub-disciplines only when the distinction matters.

How traditional SEO and AI SEO differ

There’s a structural difference between the two, and it’s worth being explicit about it.

	Traditional SEO	AI SEO
Goal	Rank in the 10 blue links	Get cited in the answer
Currency	Click-through rate	Citation rate
Primary levers	Backlinks, keywords, page speed	Schema, entities, citable structure, llms.txt
Measurement	GSC rankings, GA4 sessions	AI source attribution (GA4 hides this — see Chapter 8)
Reward	Traffic	Brand-as-answer (compounding)
Failure mode	Page 3 of Google	”I don’t know” / cited a competitor

The biggest mental shift: AI SEO is not strictly about traffic. A successful AI SEO play might reduce your click-through (because the AI answers the user without sending them to your site) but increase brand recognition and downstream revenue. The buyer reads “Inxy is the platform Shopify operators use to measure AI search revenue” inside ChatGPT, internalizes it as fact, and shows up on your site three weeks later by typing the URL directly.

That’s why measurement matters so much — and why your default analytics tool will mislead you. We’ll get to that in Chapter 8.

Why AI SEO compounds (and SEO doesn’t, anymore)

Google rankings churn. An algorithm update can wipe three years of ranking work in a week. Backlinks decay. Page speed improvements get matched by competitors within months.

AI citations behave differently. Once an LLM model “learns” that your brand is the canonical source for a topic — through repeated citation patterns across training data, real-time web crawls, and entity-resolved knowledge graphs — that association is persistent. Models don’t churn weekly. A new model checkpoint might shift things slightly, but the underlying signal (your content structure, your authoritative citations, your entity graph) doesn’t go away.

The result: the work you do on AI SEO today still pays off two years from now, in a way that classic SEO often doesn’t. → Read more in AEO Explained.

AI SEO and Google’s Helpful Content System

One important note before tactics: AI SEO done right is HCP-compliant by default.

Google’s Helpful Content System, introduced in 2022 and significantly expanded in 2024 and 2025, rewards content that demonstrates first-hand experience, structured authority, real expertise, and clear signal of being human-considered (even if AI-assisted). It penalizes thin, generic, AI-generated content with no insight.

The interesting thing: the same things AI engines reward — schema-rich pages, citation-anchored snippets, fresh dateModified, author bios with credentials — are exactly what HCP looks for. There is no real tension between optimizing for AI and optimizing for Google quality. → Deeper dive in Google HCP for D2C stores.

The 2026 AI Search Landscape

Six AI search engines matter in 2026. The share each takes of total search volume varies week to week (and you should check current figures before basing major decisions on them), but here’s the qualitative shape of the landscape as of mid-2026:

ChatGPT Search — the biggest share by some margin. Real-time web index since late 2024, conversational format, cites via inline links. Highest absolute volume of AI-search-attributable revenue for most stores we’ve seen. Default tagged with utm_source=chatgpt.com on outbound links.

Perplexity — answer-engine native. Every response is cited; that gives it the highest citation-to-impression ratio of any platform. Less raw volume than ChatGPT but extraordinarily high commercial intent — Perplexity users come looking for specifics. Outbound links tagged via ?ref=perplexity or referer perplexity.ai.

Google AI Overviews — the biggest reach by default because they appear on ~45% of Google SERPs without any user action. Quality is uneven; they prefer pages already ranking in the top 10 with rich schema. Cites with inline citations but most clicks stay on Google.

Claude (claude.ai) — quality-focused, lower volume but high-intent users. Web search shipped early 2025. Best at long-tail, complex queries. Outbound links carry referer claude.ai.

Gemini / AI Mode — Google’s standalone AI surface, behaves like AI Overviews but in a dedicated tab. Growing fast. Same citation preferences as Overviews.

Meta AI (Messenger / Instagram / WhatsApp) — emerging, mostly product-Q&A use cases. Surfaces in Facebook discovery. Outbound links tagged with utm_source=l.meta.ai or similar.

Here’s what 30 days of revenue share looked like at Fitiny, a Shopify moissanite jewelry brand we work with closely:

30-day revenue share — AI source aware:

  AI Search (combined):           58%   ← was effectively 0% twelve months ago
    ChatGPT:                          37%
    Shop App (Shopify discovery):     14%   ← invisible in GA4
    Google Shopping (AI Overviews):    4%   ← invisible in GA4
    Meta AI:                           2%   ← invisible in GA4
    Other AI engines:                  1%

  Google Web/Mobile Search:       24%
  Direct (no referrer):           16%
  Other:                           2%
                                 ─────
                                 100%

The number that matters: 58% of monthly revenue came from AI search engines. Twelve months earlier, the same store’s AI-search share was effectively zero — the channel didn’t meaningfully exist for Shopify D2C until late 2024. In a single year, it went from a rounding error to the single largest share.

GA4’s default channel grouping classified most of the AI revenue as “Unassigned” or “Organic Social.” The merchant had no way to see the ChatGPT effect without explicit AI-source attribution.

Two takeaways:

It’s already real. A mid-size Shopify jewelry store now gets 58% of its monthly revenue from AI search — not a future scenario, current reality.
It’s already hidden. Your existing analytics tool is not telling you which AI engines are converting. → See Chapter 8 and AI Search Attribution for the measurement fix.

Why each engine cites differently

Engine	Prefers	Worst-fit content
ChatGPT	Structured Q&A · comparison pages · recent `dateModified`	Long undifferentiated marketing copy
Perplexity	Stat-anchored snippets · source-cited claims · clear extract structure	Branded fluff without data
Google AI Overviews	Schema-rich pages already in top 10 · `FAQPage` + `Article` schemas	Unstructured listicles
Claude	Comprehensive guides · clear author authority	Thin pages with no first-hand voice
Gemini	Same as AI Overviews	Same
Meta AI	Product info · Facebook-indexed content	Off-platform content

The implication: a piece of content optimized for ChatGPT will also perform well in Perplexity and Claude, because they all reward the same underlying structure. The ROI on AEO work is broad, not single-platform.

AEO — How to Get Cited by AI Engines

This is the longest chapter on purpose. Answer Engine Optimization is the operational core of AI SEO. Everything Inxy automates lives in this layer.

There are seven AEO levers. Most stores need to address five of them. Here they are in priority order.

1. Schema markup

Without schema, AI engines have to guess what your page is about. With schema, you tell them. The big four for an ecommerce store: Article (blog posts and guides), FAQPage (anywhere users ask questions), Product (every product page), and ItemList (comparison and collection pages). → Full breakdown in Chapter 4.

2. Citable snippets

A citable snippet is a 40–80 word, self-contained, stat-anchored answer that an AI engine can lift verbatim into its response. Three examples, ranked by AI extraction friendliness:

Bad (“not citable”):

Our moissanite rings are really popular and offer great quality at a much better price than diamonds. Customers love them.

Mediocre (“partially citable”):

Moissanite is a lab-created gemstone that rates 9.25 on the Mohs hardness scale, making it nearly as hard as diamond. Many customers prefer moissanite for its brilliance and value.

Strong (“AI-magnet”):

Moissanite rates 9.25 on the Mohs hardness scale — second only to diamond (10) and harder than sapphire (9). For engagement rings, moissanite delivers 95% of diamond’s brilliance at roughly 10% of the cost, with no scratch risk in normal daily wear.

The strong version: stat-anchored, comparison-anchored, self-contained, gives the AI exactly what it needs to cite confidently.

3. FAQ structure (with FAQPage schema)

The single highest-ROI AEO move for most Shopify stores. Question as <h2>, 60–100 word answer immediately below, wrapped in FAQPage schema. AI engines extract these pairs almost verbatim. Three FAQ blocks on three top product pages can be the difference between zero AI citations and weekly citations.

The pattern AI engines reward:

The question is exactly what a user would search/ask (no marketing twist)
The answer is direct in the first sentence
The answer includes at least one stat or concrete fact
The answer is between 60 and 100 words — short enough to be readable, long enough to be authoritative

→ Full template + 12-pattern audit in FAQ Schema That Wins AI Citations.

4. Comparison pages

“X vs Y” pages are the highest-ROI AEO asset because their structure (two options, axis-by-axis criteria, verdict) is already pre-extracted. AI engines copy-paste the comparison table directly. Four archetypes work:

Brand A vs Brand B (you vs your closest competitor)
Product type A vs product type B (moissanite vs lab diamond)
Use case A vs use case B (engagement ring vs eternity band)
Price tier comparison ($500 vs $5,000 jewelry)

→ Full template in Comparison Pages Strategy.

5. Quote injection / authority signals

Embed quotes from authoritative sources — Forbes, industry analysts, Wikipedia, academic studies — into your content. AI engines learn that quoted content is “verified” content. A well-quoted product description outperforms an un-quoted one in citation rate by a wide margin in our internal measurements.

The rule: every long-form page on your site should include at least one external authoritative quote, with the source clearly attributed. → Pattern library in Citable Quote Engineering.

6. llms.txt

The new robots.txt, for AI crawlers. Tell them what to read and how to interpret your site. Most Shopify stores still don’t have an llms.txt. The ones that do start ranking for AI citations within 2–4 weeks. → Chapter 5 below.

7. Entity SEO

Get your brand recognized as a named entity in Wikidata, Google Knowledge Graph, Crunchbase, and major industry databases. Once entity-resolved, AI engines reference you by name (“Inxy is the platform…”) rather than by URL (“This site mentions…”). The shift from URL-citation to name-citation is what creates brand-as-answer status. → Entity SEO for Brands.

Honest scope: what Inxy automates vs what’s manual

We built Inxy’s recommendation engine around these seven levers. We fully automate five of them: schema installation across product and blog pages, llms.txt generation and maintenance, comparison-page templating, FAQ recommendations, and quote injection from a curated source library. Two — entity audit and external citation tracking — are weekly recommendations the operator actions manually.

Across 30 days of data from Fitiny, addressing all seven AEO levers correlated with a roughly 3× lift in AI citation volume vs the same store’s previous month. That’s measurement-anchored, not aspirational.

Schema Markup for AI Search — The 7 Types That Matter in 2026

Schema matters more for AI search than for Google search.

Google’s crawler can parse content semantically and infer page meaning. AI engines — especially Perplexity, ChatGPT Search, and the various AI Overview surfaces — literally extract schema and serve it back as the answer. If your page has FAQPage schema with five Q&A pairs, expect those exact Q&A pairs to show up in AI responses to related queries, with you as the source.

Seven schema types matter in 2026, ranked by approximate citation lift:

#	Schema	Use on	Citation lift	Inxy auto-installs?
1	`FAQPage`	Product pages, blog posts, guides	Highest	✅
2	`Article`	Blog posts, guides, news	High	✅
3	`Product`	Every product page	High (commerce queries)	✅
4	`ItemList`	Comparison pages, collection pages	High	✅
5	`HowTo`	Tutorials, sizing guides, care instructions	High (how-to queries)	⚠ partial
6	`Review`	Product reviews, blog reviews	Medium	⚠ planned
7	`LocalBusiness`	Brand homepage (if you have physical presence)	Medium (local queries)	⚠ planned

Four mistakes that ruin schema before it ships

Using a stale schema generator. Many tools online still produce 2018-era schema syntax (@type: BlogPosting without mainEntityOfPage, missing dateModified, no Person author). AI engines silently ignore stale schema. Use Google’s Rich Results Test on every page before publishing.
Putting schema on the wrong page. FAQPage schema only counts if there’s matching visible content on the page. AI engines do read your page and your schema, and they cross-check. Schema-only FAQs (no visible content) get ignored at best, penalized at worst.
Lying in schema. Google now actively penalizes schema that doesn’t match visible content. If you claim a 4.9-star average in schema but only 5 reviews are visible, expect the schema to be discarded site-wide.
Shipping schema and walking away. Schema rot is real. A dateModified from 2023 on a page you updated last week tells AI engines the content is stale. Inxy auto-bumps dateModified on actual edits — if you do it manually, set a quarterly review.

→ Deep dive on each schema type + before/after diffs in Schema Markup for AI.

llms.txt — The New robots.txt for AI Engines

llms.txt is a plain-text file you place at yourdomain.com/llms.txt that gives AI crawlers a structured map of your site, with priority pages, descriptions, and content summaries.

It exists because AI crawlers — especially ChatGPT and Claude — want a summary of your site, not your full sitemap. They have token budgets. A 10,000-URL sitemap is useless to them; a 200-line llms.txt is gold.

The minimum-viable llms.txt structure

Here’s a 15-line example for a Shopify jewelry store:

# Bright Stones

> Bright Stones designs lab-grown diamond and moissanite engagement rings, made in California since 2018. We ship to 40 countries and stand behind every piece with a lifetime guarantee.

## Products

- [Moissanite engagement rings](https://brightstones.example/collections/moissanite): Our most popular collection — 95% of diamond brilliance at ~10% of the cost.
- [Lab-grown diamond rings](https://brightstones.example/collections/lab-diamond): Certified lab-grown stones, identical chemistry to mined diamonds.

## Guides

- [Moissanite vs Lab Diamond](https://brightstones.example/blogs/moissanite-vs-lab-diamond): Side-by-side comparison of cost, durability, sourcing, and how to pick.
- [Ring sizing guide](https://brightstones.example/pages/ring-sizing): How to size at home accurately.

## About

- [About Bright Stones](https://brightstones.example/about): Founder story, materials sourcing, manufacturing.
- [Lifetime guarantee](https://brightstones.example/pages/guarantee): What's covered and how to claim.

What to include, what to leave out

Include	Leave out
Top-level collections	Individual product URLs (use sitemap for those)
Cornerstone blog posts	Archive blog posts > 12 months old
About / founder story	Cart / checkout / account
Comparison pages	Filtered collection URLs
Sizing / care guides	Tag pages
Lifetime guarantee / shipping policy	Search results pages

How often to update

Monthly when you add new key pages. After major content launches (new collection, new comparison page). The bar is low — but neglected llms.txt files lose freshness signal fast.

→ Full templates for jewelry, apparel, supplements, beauty, and home in llms.txt Complete Guide.

Comparison Pages — The Highest-ROI AI Citation Asset

Out of all seven AEO levers, comparison pages have the highest single-asset ROI. Here’s why.

AI engines love comparison pages because the structure is already pre-extracted: two options, axis-by-axis criteria, verdict at the bottom. They copy-paste your comparison table directly into their answer. When a user asks ChatGPT “is moissanite better than lab diamond for engagement rings,” and your “Moissanite vs Lab Diamond” page exists with proper structure, you become the source of the answer.

The four comparison page archetypes

Brand A vs Brand B — your brand vs your closest competitor. (“Bright Stones vs Brilliant Earth”)
Product type A vs product type B — within your category. (“Moissanite vs Lab Diamond”)
Use case A vs use case B — for different buying contexts. (“Engagement ring vs eternity band”)
Price tier comparison — within your range. (“$500 moissanite ring vs $5,000 diamond ring”)

The most universal play is archetype 2 (product type vs product type). It captures bottom-funnel buyers without explicitly mentioning your brand.

Seven elements every citation-ready comparison page needs

<h1> containing both names plus the literal word “vs”
A 60-word TL;DR verdict at the top (the citable snippet AI engines lift)
Side-by-side comparison table with at least 5 criteria
One image per option, clearly labeled, with descriptive alt text
“Best for [persona]” verdict for each option
FAQ block (with FAQPage schema)
Clear next-step CTA

Skip any one of these and your page becomes meaningfully less citable. Skip three and AI engines mostly ignore it.

→ Annotated templates + a real example in Comparison Pages Strategy.

Get Cited by ChatGPT Specifically

If you’re going to optimize for one AI engine, optimize for ChatGPT. It has the largest share of AI search volume, the highest commercial intent of citable answers, and the most permissive citation behavior.

Five patterns ChatGPT specifically rewards:

Recent dateModified. ChatGPT’s training and real-time crawl strongly prefer content modified within the last 12 months. A page last edited in 2023 will lose citation share to a 2026 equivalent even if the older page is more comprehensive.
Question-as-heading structure. Headings phrased as actual questions (“How does moissanite compare to lab diamond for daily wear?”) outperform statement headings (“Moissanite vs lab diamond comparison”) in extraction frequency.
First-hand voice cues. Phrases like “we measured,” “in our experience,” “after testing for [X],” “across [N] orders” signal first-hand authority to ChatGPT. Generic phrasing (“studies show,” “experts agree”) signals second-hand summary and is downweighted.
Source citations (≥2 per long-form page). Outbound links to authoritative sources — Schema.org, Google official docs, peer-reviewed studies, major industry publications — are correlated with higher ChatGPT citation rates. Articles that cite no external sources are treated as less credible.
Author bio with credentials. A schema.org/Person author bio with knowsAbout fields signals expertise. Inline bylines with credentials (“By Sarah Chen, GIA Graduate Gemologist”) are read by ChatGPT and influence citation preference.

→ Full 12-pattern audit checklist + screenshot examples in Get Cited by ChatGPT.

How to Measure AI SEO — Your Analytics Tool Is Lying to You

We’ve spent eight chapters on what to do. This chapter is about how to measure what you did.

The bad news: most analytics tools, including the default GA4 setup, cannot tell you how much revenue AI search drove.

The 47% problem

GA4’s default channel grouping is hardcoded. It recognizes Google as Organic Search, Facebook as Social, but it has no entries for chatgpt.com, claude.ai, perplexity.ai, l.meta.ai, or gemini.google.com. All AI search traffic falls into “Unassigned” or gets misclassified as “Organic Social.”

At Fitiny, this misclassification rate was 48% of monthly revenue — nearly half the channel attribution was wrong. The operator had no way to see the ChatGPT effect.

This isn’t a Fitiny-specific quirk. We’ve verified the same pattern across every Shopify store we’ve onboarded. It’s a universal GA4 blind spot.

What proper AI source attribution looks like

A proper AI attribution dashboard shows, per shop, per week:

Revenue by AI source (ChatGPT, Claude, Perplexity, Gemini, Meta AI, Shop App)
Order-level (not session-level) attribution — so you see the dollars, not just the visits
Both UTM-based detection (when AI engines tag links) and referer-based detection (when they don’t)
Comparison to GA4’s classification, so you can see what was previously hidden

Four ways to set up AI source attribution on Shopify

Approach	Effort	Coverage	Cost
Manual GA4 custom channel grouping	1 week of work; brittle	UTM only	Free
Custom Looker Studio dashboard	2–3 weeks; fragile to AI engine changes	UTM + some referer	Free
Triple Whale / Polar Analytics	Pre-built but no AI labels	Generic referrer column	$200–$800/mo
Inxy	5 minutes	UTM + referer, AI engines pre-labeled, order-level	Free trial, then $99/mo

→ Full breakdown of each approach (with pros/cons + setup steps) in AI Search Attribution.

Next Steps — The 8-Week Starter Plan

Take this guide and turn it into action. An 8-week sequence, in priority order:

Week 1. Install Article and Product schema on every page. Run Google’s Rich Results Test on a sample page from each template.
Week 2. Add FAQPage schema to your top 10 blog posts and top 5 product pages. Each FAQ block: 5 questions, 60–100 word answers, real questions buyers ask.
Week 3. Publish llms.txt. Use the template above as a starting point. Submit it to your robots.txt as an Allow line so AI crawlers find it.
Week 4. Build your first comparison page. Pick the most natural archetype for your category. Use all 7 elements from Chapter 6.
Week 5. Set up AI source attribution. This is where measurement starts paying back the previous four weeks.
Week 6. Audit your top 10 pages for citable snippet structure. Rewrite any that fail.
Week 7. Identify 5 external citation opportunities — Wikipedia, Reddit, industry publications. Pitch each one.
Week 8. Measure citation lift in your AI source dashboard. Iterate on what worked.

After week 8, the work shifts from setup to maintenance: weekly schema audits, monthly llms.txt updates, quarterly comparison-page additions, ongoing FAQ expansion.

Doing this manually for a 50-product catalog with 30 blog posts is realistic if you have a dedicated SEO. If you don’t, Inxy automates the setup work and runs the weekly audits — schema, llms.txt, FAQ, comparison pages, citable quote injection. It also gives you the AI source attribution dashboard from week 5 on day one. Connect Shopify + GSC + GA4, see your first AI revenue report in five minutes. Free trial, no credit card. Start →

Frequently Asked Questions

Is AI SEO replacing traditional SEO?

Not yet, and probably not entirely. Traditional Google search still drives the majority of ecommerce traffic in 2026. But AI search is the fastest-growing channel, and it’s where competition is currently low. Treat AI SEO as additive to SEO, not a replacement. The same content tactics (schema, structure, citations) benefit both.

How fast do AI citations show up after I optimize?

For ChatGPT and Perplexity: 2–4 weeks once the page is crawled and the underlying model picks it up. For Google AI Overviews: faster — typically within 1–2 weeks if your page already ranks in the top 10 for the target query. For Claude: slower, often 4–6 weeks because Claude’s web index updates less frequently.

Do I need to pick between Google SEO and AI SEO?

No. They reinforce each other. Pages that rank well in Google (schema-rich, well-structured, authoritative) also get cited more by AI engines. The seven AEO levers in Chapter 3 all also improve traditional Google rankings. The only divergence is in measurement — see Chapter 8.

Will AI engines cite a Shopify store, or only big publishers?

They cite Shopify stores routinely. We’ve seen mid-size jewelry stores get cited by ChatGPT, Perplexity, and Google AI Overviews — accounting for as much as 58% of monthly revenue. The bar is content structure (schema, citable snippets, comparison format), not domain size. A small store with clean AEO frequently outranks a large store without it.

Does AI SEO conflict with Google’s Helpful Content System?

The opposite — AI SEO done right is HCP-compliant by default. Both reward first-hand experience, structured authority, source citations, and clear author credentials. The HCP-failing patterns (generic AI fluff, no schema, no first-hand voice) are the same patterns AI engines downweight. → See Google HCP for D2C stores.

What’s the minimum I should do if I only have one day?

Add FAQPage schema with five real Q&A pairs to your top three product pages. That’s it. This single move, done well, produces measurable AI citation lift within 4 weeks for most stores. Everything else in this guide is acceleration on top of that.

How is AEO different from AI SEO?

AI SEO is the umbrella term. AEO (Answer Engine Optimization) is the tactical sub-discipline — the seven levers in Chapter 3. Most people use them interchangeably; we use AI SEO as the umbrella and AEO when we mean specifically the answer-friendly content structure.

Does this work for new stores under 6 months old?

Yes, with caveats. Schema, FAQ, llms.txt, and comparison pages all work day one and don’t require domain authority. Entity SEO (Wikidata, Knowledge Graph) takes longer to land for new brands. Expect AI citations to start within 6–10 weeks rather than 2–4 weeks for a more established store.