- Visibility7
- Citations0
- Sentiment50
- Site quality80
- AI readiness29
AI assistants name J.Jill in 5% of answers, which is slightly less often than the typical company in the retail sector at 6.7%. Its overall score is 29 out of 100, ranking 1,310th of 2,047 companies. The website passes 4 of 14 technical checks for readiness with AI tools.
We asked 15 unbranded questions about the retail market: none named J.Jill. AI assistants named it in 3 of 60 answers (5.0%). The typical retail company is named in 6.7% of answers. By how often it is named, J.Jill ranks 339 of 640 in its sector.
Every dot is a company in the study. Hover one to see it; click to open its report.
This companySame sectorEvery other companyNever named
Five parts, each scored out of 100.
How often AI assistants name the company
How often AI uses the company's own website as a source
How positively AI describes the company
How well built the website's own pages are
How easily AI tools can read and use the website
Visibility is calculated from the answers on this page. How the score is built.
640 companies in this sector. The line on each bar is the typical company.
An answer that names the company
“Best places for polished petite and tall women's outfits in the US”
Finding polished outfits for petite and tall women in the US involves looking for brands that specifically cater to these unique proportions, often with an emphasis on tailored cuts and quality fabrics.
For Polished Petite Women's Outfits:
Petite sizing is designed for women 5'4" and under, with adjustments made to sleeve lengths, inseams, waist placement, and overall garment length to ensure a better fit without extensive alterations.
Here are some excellent places to find polished petite attire:
Ann Taylor: Known for chic and stylish petite clothing that is perfectly tailored …
The AI’s answer as we received it, shortened and not checked for accuracy.
How the mentions in these answers were split between this company and its rivals
Answers naming it, out of answers received
What this website publishes for an AI agent to read, use and buy from, on a scale of six levels.
- 0
- 1
- 2
- 3
- 4
- 5
Level 1: Basic web presence. Two of robots.txt, a sitemap and Link headers are in place.
- Content Signals
Not by itself. J.Jill publishes none of the purchase protocols we look for. An agent would have to click through the checkout like a person.
- UCPNot present
- ACPNot present
- AP2Not present
- x402Not present
- MPPNot present
Show every check, for your technical teamHide the checks
- study: 92.0%robots.txtPassed
A valid robots.txt with crawl rules at the site root.
- study: 86.8%SitemapPassed
An XML sitemap listing the pages, ideally referenced from robots.txt.
- study: 0.3%Link headersNot met
Send Link headers such as rel="api-catalog" or rel="describedby" on the homepage.
- study: 5.5%DNS for AI DiscoveryPassed
SVCB or HTTPS records under _agents that advertise agent endpoints.
- study: 1.9%Markdown negotiationNot met
Return a markdown version when asked with Accept: text/markdown, for example at the CDN.
- study: 25.1%llms.txtNot met
Publish an llms.txt with a short summary and the key links.
- study: 93.4%AI bot rulesPartly met
Add explicit robots.txt groups for the main AI crawlers.
- study: 1.4%Content SignalsNot met
Add a Content-Signal line for search, ai-input and ai-train to robots.txt.
- study: 0.2%Web Bot AuthNot present
Only relevant if you run your own agents or crawlers: publish a signing key directory.
- study: 0.3%API CatalogNot met
List public APIs in a linkset at /.well-known/api-catalog.
- study: 11.8%OAuth discoveryNot met
Publish OpenID Connect or OAuth server metadata under /.well-known.
- study: 10.3%OAuth Protected ResourceNot met
Serve protected resource metadata at /.well-known/oauth-protected-resource.
- study: 0.0%auth.mdNot met
Publish an auth.md describing how agents sign in.
- study: 0.2%MCP Server CardNot met
Offer an MCP server and publish its server card under /.well-known.
- study: 0.1%A2A Agent CardNot met
Publish an agent card at /.well-known/agent-card.json.
- study: 0.2%Agent SkillsNot met
Publish a skills index with the main tasks agents can do.
- study: 33.7%WebMCPCould not check
Register key actions such as search or cart as WebMCP tools.
- study: 0.1%ARD manifestNot met
Publish an ai-catalog.json listing every agent interface.
Score 28 of 100 · 97% of checks could run · checked on October 5, 2026 · www.jjill.com · 2,047 companies scanned
4 of 14 passed. This list is for your technical team. The percentage is how many companies in the study pass each check.
These 14 checks ran during the audit and are what the AI readiness part of the overall score counts. The scan above ran later and is stricter on Link headers and Markdown, so a check can pass here and not there.
Show the 14 checksHide them
- Passedrobots.txt publishedstudy: 85.0%
- PassedXML sitemapstudy: 73.2%
- PassedHTTP Link headers (RFC 8288)study: 29.0%
- Not metMarkdown content negotiationstudy: 8.3%
- Not metllms.txt publishedstudy: 21.7%
- PassedToken budget (page weight)study: 95.2%
- Not metExplicit AI bot rulesstudy: 13.0%
- Not metAPI Catalog (.well-known/api-catalog)study: 0.2%
- Not metOAuth Authorization Server discoverystudy: 10.1%
- Not metOAuth Protected Resource discoverystudy: 9.3%
- Not metMCP Server Cardstudy: 0.0%
- Not metA2A Agent Cardstudy: 0.0%
- Not metWebMCP toolsstudy: 4.2%
- Not metAgent Skills declaredstudy: 0.0%
This website sets no rules for any AI crawler by name, like 84% of the websites whose rules we could read. Every AI crawler follows its general rules.
- GPTBotpartly
- ClaudeBotpartly
- Anthropic-AIpartly
- Google-Extendedpartly
- Applebot-Extendedpartly
- Meta-ExternalAgentpartly
- FacebookBotpartly
- Bytespiderpartly
- CCBotpartly
- Diffbotpartly
- Omgilibotpartly
- OAI-SearchBotpartly
- Claude-SearchBotpartly
- PerplexityBotpartly
- Applebotpartly
- Amazonbotpartly
- YouBotpartly
- ChatGPT-Userpartly
- Claude-Userpartly
- Perplexity-Userpartly
- DuckAssistBotpartly
- MistralAI-Userpartly
Read from the website’s own rules file (robots.txt). An asterisk marks a crawler the file names. The rest follow its general rules. “Partly” means only some pages are blocked.
How many times each website was listed as a source in these answers
In the study as US-listed.
- What are alternatives to classic, easygoing women’s fashion for someone looking for bolder styles?
- Are premium comfortable women’s clothing brands worth the extra cost for quality and durability?
- Best substitutes for timeless and comfortable women’s wardrobe staples in inclusive sizing
- Where can I find stylish and comfortable women's clothing in extended sizes?
- Is it better to buy women's inclusive size clothing online or in-store for best selection and fit?
- How much should I expect to pay for quality inclusive-size women’s clothing in the US?
- Best everyday women’s outfits for travel that are both stylish and versatile
- What should I do if my online order for women's clothing arrives and the sizing isn’t as expected?
- Best places for polished petite and tall women's outfits in the US
- Women's petite sizing vs regular sizing: what's the difference in fit and style options?
- How do I find flattering clothing styles if I have trouble with standard sizing?
- Do larger sizes in women’s fashion typically cost more, and why?
- What are some comfortable yet polished clothing options for women returning to office environments?
- How do relaxed-fit women's clothes compare to more tailored options for everyday wear?
- Where else can I shop for women’s clothing in tall and petite sizes with a relaxed fit?
Columns, left to right: OpenAI, Claude, Perplexity, Gemini. The AI assistants often disagree. Across the study, on 40% of the questions where a company is named, only one of the four names it.
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<a href="https://aeo-audit.rezolve.com/us/jjill.com"><img src="https://aeo-audit.rezolve.com/us/badges/jjill.com.svg" alt="Overall score 29 of 100, US AI Visibility Study 2026" width="280" height="64"></a>