Rolex

www.rolex.com

Last scanned Jun 20, 2026, 6:19 AM

Verdict

Closed to AI agents

ARC Score v1.0

38/100Restricted

How it's computed →

Agent access16.7/50

Structured data11/25

Protocol files0/15

Scan stability10/10

Agent access

Agent	Status	Source
GPTBot	Blocked	UA test: blocked by site defenses
ChatGPT-User	Blocked	UA test: blocked by site defenses
ClaudeBot	Blocked	UA test: blocked by site defenses
Claude-Web	Allowed	robots.txt: Allow
PerplexityBot	Blocked	UA test: blocked by site defenses
Google-Extended	Blocked	UA test: blocked by site defenses
Amazonbot	Blocked	UA test: blocked by site defenses
Bingbot	Blocked	UA test: blocked by site defenses
CCBot	Blocked	UA test: blocked by site defenses

Infrastructure

PlatformsalesforceCDN—WAFnone

Data signals

JSON-LDDetected
Schema.org ProductNot detected
Open GraphNo
Product feedNo
llms.txtNo

Change history · last 90 days

0 changes

No confirmed changes in the last 90 days — this brand's agent access posture has been stable.

The free public window is 90 days. Multi-year history, watchlists, and change alerts are part of Pro.

Fix it with Claude Code

Each failed check below has a ready-made prompt tailored to this scan's findings. Run it in Claude Code from your site's repository.

✗WAF/CDN blocks 8 agents despite robots.txt allowing themshow prompt

My e-commerce site is rolex.com. Our robots.txt does not block these AI agents, but live HTTP tests show our WAF/CDN returns 403s or challenge pages to them:

- GPTBot (OpenAI — ChatGPT training)
- ChatGPT-User (OpenAI — ChatGPT live browsing)
- ClaudeBot (Anthropic — Claude training)
- PerplexityBot (Perplexity — Perplexity / Comet)
- Google-Extended (Google — AI Mode / Gemini)
- CCBot (Common Crawl — Open training data)
- Amazonbot (Amazon — Buy For Me)
- Bingbot (Microsoft — Copilot / Bing)

This means our stated policy (allow) and our enforcement (block) disagree, and AI assistants silently fail on our store. In this repository and our infrastructure config:

1. Search for bot-management or firewall configuration (Cloudflare rules in code/terraform, vercel.json, middleware that filters user-agents, security headers config).
2. Where we control it in code, add allowlist entries for the user-agents above — scoped to product, category, and content pages only; keep protections on /cart, /checkout, /account, and admin routes.
3. Show me the diff, and flag any rule you find that blanket-blocks "bot-like" traffic.
4. If the blocking happens in a dashboard we don't keep in code (e.g. Cloudflare Super Bot Fight Mode, DataDome, PerimeterX), tell me the exact product setting to change and what to set it to.

After deploy, verify with: curl -A "GPTBot/1.0" -I https://rolex.com/ (expect 200, not 403).

✗No Schema.org Product markupshow prompt

My e-commerce site is rolex.com. A scan found JSON-LD on the site but no Schema.org Product markup, so AI shopping agents can't reliably read our product names, prices, availability, or images. The site runs on salesforce, so prefer that platform's idiomatic way of serving these files/markup (theme templates, app settings, or metafields) over hand-rolled middleware where it exists.

In this repository:

1. Find the product page template/component and add a JSON-LD <script type="application/ld+json"> block with Schema.org Product markup: name, description, image, sku, brand, and an Offer with price, priceCurrency, availability (use schema.org/InStock | OutOfStock), and url. Populate every field from our real product data — no placeholders.
2. Add an Organization JSON-LD block to the base layout (name, url, logo) if missing.
3. If we have category/listing pages, add ItemList markup referencing the product URLs.
4. Show me one fully rendered example of the JSON-LD for a real product, then validate the shape against Google's Rich Results requirements for Product and fix any warnings you can detect statically.

Verify after deploy with https://search.google.com/test/rich-results on a product URL.

✗No Open Graph tagsshow prompt

My e-commerce site is rolex.com. A scan found no Open Graph meta tags, so link previews and many AI agents see untitled, imageless pages. The site runs on salesforce, so prefer that platform's idiomatic way of serving these files/markup (theme templates, app settings, or metafields) over hand-rolled middleware where it exists.

In this repository:

1. Add og:title, og:description, og:image, og:url, and og:type to the base layout's <head>, with sensible site-wide defaults.
2. On product pages, override them per product: og:type "product", the product image as og:image (absolute URL, ≥1200×630 where available), and the live price in og:description.
3. Add twitter:card "summary_large_image" alongside.
4. Show me the diff and one rendered <head> for a real product page.

✗No llms.txtshow prompt

My e-commerce site is rolex.com. We don't publish an llms.txt file yet — the emerging convention (llmstxt.org) that gives language models a concise, curated guide to a site.

In this repository:

1. Create /llms.txt (served at https://rolex.com/llms.txt as text/plain or text/markdown) following the llms.txt format:
   - H1 with our brand name,
   - a one-paragraph blockquote summary of what we sell and who we serve,
   - sections linking to our most useful pages for an AI agent: bestsellers/category pages, shipping & returns policy, size guides, FAQ/support, and store locator if any.
2. Write the summary from this repository's real content (README, about page, homepage copy) — keep it factual, no marketing superlatives.
3. Keep it under ~200 lines, every link absolute.
4. Show me the full file content and where you wired it to be served.

Verify after deploy: curl https://rolex.com/llms.txt

✗No machine-readable product feedshow prompt

My e-commerce site is rolex.com. A scan found no machine-readable product feed, which feed-based AI shopping agents (ChatGPT Shopping, Klarna, Google AI Mode) rely on. The site runs on salesforce, so prefer that platform's idiomatic way of serving these files/markup (theme templates, app settings, or metafields) over hand-rolled middleware where it exists.

In this repository:

1. Determine where product data lives (database models, CMS, platform API) and add a product feed endpoint — Google Merchant–compatible XML (RSS 2.0 with the g: namespace) at /feeds/products.xml, or JSON if that's more idiomatic here.
2. Include per item: id, title, description, link, image_link, price with currency, availability, brand, and gtin/mpn when we have them.
3. Paginate or stream if the catalog is large; cache for ~1 hour.
4. Link the feed from robots.txt or a <link rel="alternate"> in the layout, show me the diff, and print the first two feed items rendered from real data.

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Markdown

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Updates automatically with each daily scan.

Scanned daily via robots.txt parsing and live HTTP tests for 9 AI agents. Changes are confirmed across two consecutive scans before publishing. Read the full methodology →