- Visibility18
- Citations1
- Sentiment77
- Site quality73
- AI readiness29
AI assistants name Ann Taylor in 18.3% of answers, which is more often than the typical company in its sector. The company's overall score is 36 out of 100, ranking 718th of 2,047 companies. Its website passes 2 out of 14 technical checks showing how ready it is for AI tools.
We asked 15 unbranded questions about the retail market: none named Ann Taylor. AI assistants named it in 11 of 60 answers (18.3%). The typical retail company is named in 6.7% of answers. By how often it is named, Ann Taylor ranks 153 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
“Are frequent sales common in women's professional clothing stores?”
Yes, frequent sales are common in women's professional clothing stores, aligning with the broader fashion industry's sales cycles. These sales are driven by several factors:
Seasonal Collections:
The fashion industry operates on seasonal collections, with new designs introduced multiple times a year. To make room for new inventory, "old" collections are put on clearance or discounted.
Typical Sale Calendar:
Most fashion brands and retailers follow a general sale calendar that includes:
Winter Sale: December to February.
Spring Sale (Mid-season): March to A …
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 0: Not yet agent-ready. Agents struggle to find basic information about the site.
- 2 of robots.txt, Sitemap, Link headers
Not by itself. Ann Taylor 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
The shop runs on Salesforce Commerce Cloud.
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%SitemapNot met
Serve an XML sitemap and reference it 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 DiscoveryNot present
Advertise agent endpoints with SVCB records under _agents.
- 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 16 of 100 · 97% of checks could run · checked on October 5, 2026 · www.anntaylor.com · 2,047 companies scanned
2 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%
- Not metXML sitemapstudy: 73.2%
- Not metHTTP 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 retailers: listed as a retailer in Wikidata, a public database, and among the websites Cloudflare Radar ranks as visited.
- What are the best outfit ideas for transitioning from office to weekend in women's apparel?
- Women's suiting separates vs. coordinated sets — which offers more versatility?
- What are alternatives to traditional women's suits for professional settings?
- Is it worth investing in high-quality blazers for workwear?
- Are frequent sales common in women's professional clothing stores?
- What should I do if my new trousers for work don't fit as expected?
- What are some stores that offer versatile everyday women's attire beyond basics?
- Are there loyalty programs or rewards that help save money on women's apparel?
- How do you keep lightweight sweaters looking new after multiple washes?
- How can you build a capsule wardrobe for year-round business casual looks?
- Where can I shop for stylish business casual attire for women in the US?
- Where can I find affordable options for women's blouses suitable for work?
- What is the average price range for quality women's suiting in the US?
- How does business casual differ from classic professional dress for women?
- What are the best places to find women's professional outfits for the office?
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.
A badge of this result that you can show on your own website. Copy the code below.
<a href="https://aeo-audit.rezolve.com/us/anntaylor.com"><img src="https://aeo-audit.rezolve.com/us/badges/anntaylor.com.svg" alt="Overall score 36 of 100, US AI Visibility Study 2026" width="280" height="64"></a>