- Visibility42
- Citations5
- Sentiment69
- Site quality80
- AI readiness29
AI assistants name Target in 43.3% of answers, much more often than the typical company in retail, which is named in 6.7% of answers. Target's overall score is 44 out of 100, ranking 128th of 2,047 companies studied. Its website passes 4 of 14 technical checks for readiness for AI tools.
We asked 15 unbranded questions about the retail market: none named Target. AI assistants named it in 26 of 60 answers (43.3%). The typical retail company is named in 6.7% of answers. By how often it is named, Target ranks 35 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
“Where else can families find affordable clothing besides major retail chains?”
Families can often find affordable clothing at discount and budget-friendly kids’ retailers, resale marketplaces/secondhand shops, and sale or clearance sections from family-focused brands, not just major retail chains.
Good options include:
Secondhand marketplaces and clothing swaps for the lowest prices on basics like tees, leggings, denim, jackets, and shoes.
Family-oriented budget brands such as PatPat, The Children’s Place, Old Navy, H&M, Target, and Carter’s for everyday wear and matching family outfits.[2 …
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. Target 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 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.txtPassed
An llms.txt that tells language models what the site offers.
- 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%WebMCPPassed
Tools registered in the browser through navigator.modelContext.
- study: 0.1%ARD manifestNot met
Publish an ai-catalog.json listing every agent interface.
Score 38 of 100 · 100% of checks could run · checked on October 5, 2026 · www.target.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%
- Not metHTTP Link headers (RFC 8288)study: 29.0%
- Not metMarkdown content negotiationstudy: 8.3%
- Passedllms.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 S&P 500. Number 8 on the NRF Top 100 Retailers 2026 list, ranked by US retail sales.
- How can I resolve issues when my curbside order is delayed?
- Discount chain vs. supermarket: which is better for weekly grocery shopping?
- Where else can families find affordable clothing besides major retail chains?
- Do mass-market chains offer good deals on back-to-school shopping for families?
- How do online mass retailers compare to in-store options for convenience and selection?
- What are typical price ranges for kids' clothing at large retail chains?
- What should I do if I place an online order and part of it is missing on pickup?
- Which retailers offer same-day pickup or delivery for household staples in my area?
- How do multi-category retailers price electronics compared to specialty stores?
- Alternatives to big box stores for buying groceries and home goods in the US?
- Is it worth shopping at a large variety retailer for bulk household essentials?
- How do family shoppers make the most of one-stop shopping for weekly essentials?
- What are efficient ways to shop for holiday decor and gifts at a multipurpose store?
- Best places for affordable Halloween costumes and seasonal decor this year?
- Where can I find a wide selection of groceries, clothes, and electronics in one store nearby?
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/target.com"><img src="https://aeo-audit.rezolve.com/us/badges/target.com.svg" alt="Overall score 44 of 100, US AI Visibility Study 2026" width="280" height="64"></a>