Louis Vuitton

www.louisvuitton.com
Last scanned Jun 20, 2026, 6:15 AM
Verdict
Closed to AI agents
ARC Score v1.0
27/100Restricted
How it's computed →
Agent access16.7/50
Structured data0/25
Protocol files0/15
Scan stability10/10

Agent access

AgentStatusSource
GPTBotBlockedUA test: blocked by site defenses
ChatGPT-UserBlockedUA test: blocked by site defenses
ClaudeBotBlockedUA test: blocked by site defenses
Claude-WebAllowedrobots.txt: Allow
PerplexityBotBlockedUA test: blocked by site defenses
Google-ExtendedBlockedUA test: blocked by site defenses
AmazonbotBlockedUA test: blocked by site defenses
BingbotBlockedUA test: blocked by site defenses
CCBotBlockedUA test: blocked by site defenses

Infrastructure

PlatformcustomCDNakamaiWAFakamai

Data signals

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

Change history · last 90 days

47 changes
  1. Apr 5, 2026
    CCBot robots.txtallowed→no_rule
    Amazonbot robots.txtallowed→no_rule
    robots.txt presencetrue→false
    Claude-Web robots.txtallowed→no_rule
    PerplexityBot robots.txtallowed→no_rule
    Google-Extended robots.txtallowed→no_rule
    ChatGPT-User robots.txtallowed→no_rule
    ClaudeBot robots.txtallowed→no_rule
    GPTBot robots.txtallowed→no_rule
    Amazonbot robots.txtno_rule→allowed
    robots.txt presencefalse→true
    Claude-Web robots.txtno_rule→allowed
    PerplexityBot robots.txtno_rule→allowed
    Google-Extended robots.txtno_rule→allowed
    CCBot robots.txtno_rule→allowed
    ChatGPT-User robots.txtno_rule→allowed
    ClaudeBot robots.txtno_rule→allowed
    GPTBot robots.txtno_rule→allowed
  2. Apr 4, 2026
    Google-Extended robots.txtallowed→no_rule
    CCBot robots.txtallowed→no_rule
    Amazonbot robots.txtallowed→no_rule
    robots.txt presencetrue→false
    ClaudeBot robots.txtallowed→no_rule
    Claude-Web robots.txtallowed→no_rule
    PerplexityBot robots.txtallowed→no_rule
    GPTBot robots.txtallowed→no_rule
    ChatGPT-User robots.txtallowed→no_rule
  3. Mar 30, 2026
    CCBot robots.txtno_rule→allowed
    Amazonbot robots.txtno_rule→allowed
    robots.txt presencefalse→true
    ChatGPT-User robots.txtno_rule→allowed
    ClaudeBot robots.txtno_rule→allowed
    Claude-Web robots.txtno_rule→allowed
    PerplexityBot robots.txtno_rule→allowed
    Google-Extended robots.txtno_rule→allowed
    GPTBot robots.txtno_rule→allowed
  4. Mar 29, 2026
    ClaudeBot accessblocked→no_rule
    PerplexityBot accessblocked→no_rule
    Google-Extended accessblocked→no_rule
    CCBot accessblocked→no_rule
    Amazonbot accessblocked→no_rule
    cdnakamai→unknown
    wafakamai→none-detected
    blocked_agent_count8→0
    GPTBot accessblocked→no_rule
    ChatGPT-User accessblocked→no_rule
  5. Mar 28, 2026
    Claude-Web accessallowed→no_rule

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 louisvuitton.com. Our robots.txt does not block these AI agents, but live HTTP tests show our WAF/CDN (detected: akamai) 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://louisvuitton.com/ (expect 200, not 403).
✗No JSON-LD structured datashow prompt
My e-commerce site is louisvuitton.com. A scan found no JSON-LD structured data at all, so AI shopping agents can't reliably read our product names, prices, availability, or images.

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 louisvuitton.com. A scan found no Open Graph meta tags, so link previews and many AI agents see untitled, imageless pages.

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 sitemap.xmlshow prompt
My e-commerce site is louisvuitton.com. A scan could not find a sitemap.xml, so crawlers and AI agents have no reliable way to discover our product pages.

In this repository:

1. Generate a sitemap.xml covering the homepage, category pages, and every product page, with <lastmod> from each product's updated-at where available.
2. If the catalog is large, split into a sitemap index with child sitemaps of ≤50,000 URLs.
3. Reference the sitemap from robots.txt ("Sitemap: https://louisvuitton.com/sitemap.xml").
4. Make it regenerate automatically (build step or on-demand route) rather than a one-off static file, and show me the diff.
✗No llms.txtshow prompt
My e-commerce site is louisvuitton.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://louisvuitton.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://louisvuitton.com/llms.txt
✗No machine-readable product feedshow prompt
My e-commerce site is louisvuitton.com. A scan found no machine-readable product feed, which feed-based AI shopping agents (ChatGPT Shopping, Klarna, Google AI Mode) rely on.

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.

Embed this score

ARC Score badge for Louis Vuitton
HTML
<a href="https://www.arcreport.ai/brand/louis-vuitton"><img src="https://www.arcreport.ai/badge/louis-vuitton.svg" alt="ARC Score for Louis Vuitton" height="22"></a>
Markdown
[![ARC Score for Louis Vuitton](https://www.arcreport.ai/badge/louis-vuitton.svg)](https://www.arcreport.ai/brand/louis-vuitton)

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 →