How AI-ready are independent ski shops? The Winter 2026/27 benchmark of 162 stores

Short answer: We ran 162 independent US ski and snowboard shops through sqrly Pulse, our AI-readiness assessment. The median shop scores 56 out of 100, the highest 74, and no shop reached the top band. When we asked four AI assistants to recommend a shop in each store's category, 4 in 5 shops weren't named by any of them. Shopify stores post a median AI Search Visibility of 70, stores on other or undetected platforms 44. Off Shopify, one thing separates the leaders: 94% of the top-scoring quarter publish structured product data, and none of the bottom quarter do.

Ski season is when specialty shops make their year, and more of this season's shoppers will start with an AI assistant instead of a search box: "what's a good all-mountain ski for an intermediate," "where can I get boots fitted near Bend." Whether the assistant can read a shop's website well enough to name it in that answer is a new, mostly invisible question. So we measured it, across an entire category of independent retailers, in one short window.

What we did

Between September 23 and 30, 2026, we ran every shop on our list through the same sqrly Pulse assessment, on one fixed scoring build. The list is independent US ski and snowboard specialty retailers: single shops and small chains, compiled from industry member directories (including SIA and the Grassroots Outdoor Alliance), retailer directories, and web search. We also scanned four national chains as a reference. They're reported separately below and are not part of any other number in this report.

For each shop, the assessment reads the public website the way an AI system would (pages, product data, structured data, policies) and asks four assistants (Claude, ChatGPT, Gemini, and Perplexity) about it live, with web search on. The overall score runs from 0 to 100. The full methodology is at the end.

Finding 1: most shops sit in the middle, and nobody is at the top yet

Overall scores, 162 shops

Invisible to Agents (0–29)Not Yet Ready (30–49)Partially Ready (50–69)Mostly Ready (70–84)Agent-Ready (85–100)

The median is 56, and the middle half of shops score between 32 and 68. The distribution has two humps: a large group at 60–69 and a second group between 10 and 39. As the next findings show, that split mostly follows the platform a shop runs on. The highest score is 74.

That's not a knock on these shops. Being fully legible to AI systems is new, and almost nobody has finished the job. The upside is that the gap between an average shop and a top one is a handful of fixable things, not a rebuild.

(The band names are the assessment's own labels for score ranges. "Invisible to Agents" means a score under 30. It doesn't mean an assistant can't find the shop at all.)

Finding 2: when shoppers ask for a shop, assistants rarely name these stores

For each shop we asked every assistant a category question with no shop named, the way a shopper would ("I'm looking for ski and snowboard gear — which specific shops or brands should I buy from?", with the category taken from the shop's own site), in a fresh conversation with web search on.

• 132 of 162 shops (81%) weren't named by any of the four assistants.
• 13 were named by one assistant, 6 by two, 7 by three, and 4 by all four.
• Each assistant named only between 6% and 13% of the shops.

This is the noisiest number in the report: it's one live question per assistant per shop, and the same question can get a different answer an hour later. Read it as a pattern, not a per-shop verdict. The pattern is consistent though. The names assistants gave instead were mostly national retailers and brands: REI, Patagonia, evo, Backcountry, The North Face, Arc'teryx, Christy Sports, and Burton came up most often.

Finding 3: the platform a shop runs on lines up with a big gap

Median AI Search Visibility by platform

We compare platforms on AI Search Visibility, which measures what any assistant can learn from a shop's public website and is scored the same way for every shop. (The overall score also includes a platform-shopping channel we can only assess on Shopify and WooCommerce, so overall scores aren't a fair platform comparison.)

This is a description, not a verdict on any platform: the groups differ in more ways than their software. But part of the gap is concrete. Shopify adds structured product data to every product page and publishes an llms.txt file (a short guide for AI systems) by default. In this benchmark, 100% of Shopify shops had structured product data and 98% had an llms.txt. On other or undetected platforms, it was 54% and 12%.

Finding 4: what the leaders do differently

Because the platform does so much of the work, we compared the highest- and lowest-scoring quarter of shops within each platform group.

Off Shopify, the dividing line is stark. Among the 72 shops on other or undetected platforms, scores range widely, and the top quarter has the basics of machine-readable data in place where the bottom quarter mostly doesn't:

Off Shopify: what the top quarter has that the bottom quarter doesn't (72 shops)

Top quartile (18 shops, scores 39+)Bottom quartile (18 shops, scores 22 or less)

These features are part of what the assessment checks, so of course shops that have them score higher. The useful part is which ones: structured product data and organization data are the largest gaps, and both are things any platform can add, usually with an app, a plugin, or a template change.

On Shopify, scores cluster tightly. The middle half of Shopify shops score between 66 and 69, a spread about the size of the run-to-run noise in a single scan. The platform sets most of the score. What the top Shopify shops had more often: FAQ structured data (29% of the top quarter vs 5% of the bottom), social profiles linked in their structured data (54% vs 29%), and stronger imagery (a median Imagery & media score of 65 vs 48).

One honest oddity: off Shopify, top-quarter shops were more likely than bottom-quarter shops to block at least one AI crawler in robots.txt (39% vs 11%). We don't have an explanation, and with 18 shops in each group it may be coincidence. It's not a reason to block AI crawlers: a blocked crawler can't read what you publish.

Finding 5: where the points go missing

The AI Search Visibility score is built from five categories. The median shop's results:

Imagery is the weakest area across the board: product photos without descriptive alt text, and product details that live only in images. Brand & company knowledge is next to last for a common reason: the basics an assistant needs to describe a shop (phone number, location, social profiles) are on the page for people but missing from the structured data machines read. Only 12% of shops include a phone number in their organization data.

Finding 6: what a site says is ahead of how it sells

Median score by channel

sqrly Pulse scores two things separately. AI Search Visibility is what any assistant can learn from a shop's public website on its own. Here the median shop scores 63. Your Own AI Agent is whether a shop has an AI that knows its products and can sell in conversation, on its site and wherever shoppers ask. Here the median is 18.

That second gap is exactly the one sqrly is built to close, so read it with that in mind. But the pattern is real: the knowledge that makes a specialty shop worth visiting (boot fitting, local conditions, which ski actually suits which skier) mostly lives in staff heads and static pages, not anywhere an AI can hold a conversation from. About a third of shops (34%) run a chat widget, but almost all of those are live chat with staff. We found exactly one AI assistant among them.

Our Shopify app is how we're closing that gap. It gives a shop its own selling agent, a digital twin of the shop: it knows every product, size and stock level, learns the shop's own knowledge (boot-fitting advice, demo programs, tuning turnaround), answers shoppers on every page of the store with product cards, and never recommends what's sold out. AI shopping agents can consult it on the store's Ask our expert page and through site tools. It's in early pilots now, and it only works for shops on Shopify, about half of the shops in this benchmark.

The national chains, for reference

We scanned four national chains alongside the independents. Christy Sports scored 46. For Backcountry, evo, and REI, our scanner's requests were refused on two attempts about a day or more apart, so we have no score for them.

That isn't a finding about AI assistants. Our scanner runs from a cloud data center, and many large sites refuse automated traffic from data centers by default. Assistants that browse on a user's behalf can see something different. It does show that the national players are harder for automated readers to get at than most independents.

The shops we couldn't score

Why 51 listed shops weren't scored

Of the 214 independent shop sites on our final list, 162 were scored and ranked. We don't guess at the rest, so they're counted separately and never given a number:

• 23 had no online store: brochure sites or shops that sell in store only. Not a criticism; the assessment measures online stores, so a score would be meaningless.
• 18 blocked our scanner on two attempts about a day or more apart, the same data-center refusal described above. Many of these are on hosting setups that refuse cloud traffic by default.
• 1 tells crawlers to stay out in its robots.txt while letting search engines in. We honor robots.txt, so we didn't read it.
• 9 turned out not to be operating shops at scan time: parked or offline domains (4), stores that were closing or locked (2), duplicate location sites of a shop already in the list (2), and one shop absorbed into another business.
• 1 was a rentals site whose online store lives on a separate domain, which we scored instead.

The top-scoring shops

Eighteen shops scored 70 or higher, the Mostly Ready band. All eighteen run on Shopify. Scores vary by a couple of points between runs, so we list them by score and don't rank within it.

What they have in common: all 18 run on Shopify, which adds structured product data and an llms.txt file to every store by default, and 16 of 18 publish organization data that tells a machine who they are. None of them reached the top band. The next steps up from here are the ones in Finding 4: FAQ structured data, linked social profiles, descriptive alt text, and an assistant that can answer product questions in conversation.

What any shop can do this week

These are the fixes that line up with the biggest differences in the data, roughly in order of impact. None of them requires sqrly or a new platform.

1. Publish structured product data on every product page: name, price, availability, brand, and a product identifier (GTIN/UPC) where you have one. Shopify does this by default. On other platforms it's usually an app, a plugin, or a template setting.
2. Add organization data to your homepage: your shop's name, address, phone number, and links to your social profiles. Assistants use it to describe who you are and where you are.
3. Make sure you have an XML sitemap and that your robots.txt points to it. It's how automated readers find every product.
4. Check your robots.txt for rules that block AI crawlers. If you block them on purpose, that's your call; if a plugin or template did it for you, it's worth knowing.
5. Write alt text for product photos that says what's in the picture ("Nordica Enforcer 94, 2026, side view"), not just the file name.
6. Answer the questions shoppers ask (returns, shipping, boot fitting, demo programs) in plain text on your site, ideally on an FAQ page with FAQ structured data.

Is your shop in the benchmark?

Every shop we scored has its own report. If you run one of them and haven't seen yours, get in touch and we'll send your shop's report and where it placed.

Not in the benchmark? Run a free sqrly Pulse snapshot on your own store: no account, no card. It takes a few minutes and shows what AI assistants can and can't read about your shop, and what to fix first.

Become a pilot shop. We're looking for a handful of stores where good advice decides the sale (on Shopify today, more platforms soon), and a ski shop, where fit and the right gear for the skier are the whole conversation, is exactly that. Pilot shops get their own private install of the sqrly digital twin and work directly with the founder, and features they ask for can ship within days. Email florian@sqrly.ai with your shop's URL.

Methodology

• Population. Independent US ski and snowboard specialty retailers with a transactional online store, compiled from industry member directories, retailer directories, and web search, then reviewed by hand. Big-box retailers, marketplaces, brand-direct stores, and pure rental operations were excluded. Four national chains were scanned as a separate, unranked reference.
• One build, one window. Every shop was scored by the same assessment build between September 23 and 30, 2026. Bug fixes made during the window affected only whether a scan completed or was excluded, never how a completed scan was scored.
• What the score measures. The overall score blends three channels: AI Search Visibility (five categories, measured the same way for every shop), Platform Shopping (live product-feed and agent checks, available only for Shopify and WooCommerce stores), and Your Own AI Agent. Assistant answers were collected live from Claude, ChatGPT, Gemini, and Perplexity with web search on.
• Exclusions are counted, not scored. Sites with no online store, sites that blocked our scanner twice, sites whose robots.txt asked us not to read them, and sites that weren't operating shops at scan time are reported above and never given a number.
• Reproducible. Every number in this report is computed by a script from the stored scan data, and the charts are generated from the same data.
• Scanner update after the study (October 9, 2026). Shopify now publishes a default llms.txt file on every store automatically. Our scanner used to credit that file as if the shop had written it; since October 9 it no longer does. The scores in this report are the benchmark snapshot from the build used September 23–30, and they are unchanged. On today's scanner, Shopify shops score roughly 4–5 points lower. Shops on other platforms aren't affected by this change.

How to read these numbers

• One scan per shop. Scores vary by a couple of points between runs, so read bands and medians, not decimals.
• A snapshot. Assistant answers change over time and between sessions; these reflect what we observed in the scan window.
• Descriptive. These results show what the data looks like. They don't prove that any single change causes a higher score.
• About AI readability, not shop quality. A low score says nothing about a shop's gear, service, or staff. It says how much of that an AI system can currently read.
• Updated October 2. This report replaces our early results from September 30, which reported a median of 53 before sites that weren't operating shops were removed from the count.