Saatva

www.saatva.com
Last scanned Jun 20, 2026, 6:20 AM
Verdict
Open to AI agents
ARC Score v1.0
71/100Mostly open
How it's computed →
Agent access50/50
Structured data11/25
Protocol files0/15
Scan stability10/10

Agent access

AgentStatusSource
GPTBotAllowedrobots.txt: Allow
ChatGPT-UserAllowedrobots.txt: Allow
ClaudeBotAllowedrobots.txt: Allow
Claude-WebAllowedrobots.txt: Allow
PerplexityBotAllowedrobots.txt: Allow
Google-ExtendedAllowedrobots.txt: Allow
AmazonbotAllowedrobots.txt: Allow
BingbotAllowedrobots.txt: Allow
CCBotAllowedrobots.txt: Allow

Infrastructure

PlatformwoocommerceCDNcloudfrontWAFnone

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.

✗No Schema.org Product markupshow prompt
My e-commerce site is saatva.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 woocommerce, 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 saatva.com. A scan found no Open Graph meta tags, so link previews and many AI agents see untitled, imageless pages. The site runs on woocommerce, 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 saatva.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://saatva.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://saatva.com/llms.txt
✗No machine-readable product feedshow prompt
My e-commerce site is saatva.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 woocommerce, 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.

Embed this score

ARC Score badge for Saatva
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Markdown
[![ARC Score for Saatva](https://www.arcreport.ai/badge/saatva.svg)](https://www.arcreport.ai/brand/saatva)

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 →