- Visibility11
- Citations0
- Sentiment50
- Site quality67
- AI readiness29
AI assistants name Lear in 8.3% of answers, which is slightly more often than the typical company in the automotive sector. Lear’s overall score is 27 out of 100, ranking 1,522nd of 2,047 companies. Its website passes 4 of 14 technical checks for readiness for AI tools.
We asked 15 unbranded questions about the automotive market: none named Lear. AI assistants named it in 5 of 60 answers (8.3%). The typical automotive company is named in 5.0% of answers. By how often it is named, Lear ranks 18 of 49 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.
49 companies in this sector. The line on each bar is the typical company.
An answer that names the company
“Best automotive seating solutions for fleet vehicles focused on durability”
Good — here’s a concise, practical guide for choosing the most durable seating solutions for fleet vehicles (service vans, delivery trucks, police/patrol, transit, work trucks). I cover materials, seat construction/features to specify, maintenance/upkeep, and reliable vendors/sources to evaluate.
Top principles (why they matter)
Face material drives daily wear, cleanability and UV resistance (most common failure mode = cover wear/exposure → foam damage). (bigrigseats.com)
Understructure (frame, rails, foam) determines lifetime under high-use …
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
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.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 25 of 100 · 97% of checks could run · checked on October 5, 2026 · www.lear.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 Consumer brands.
- Alternatives to fully integrated electronic seating systems for automakers
- Top automotive electronic system providers for connected cars
- Cost differences between standard and premium in-car electronic systems
- Best automotive seating solutions for fleet vehicles focused on durability
- Common problems with smart automotive seat controls and how to fix them
- Typical price range for OEM-level automotive seating solutions in the US
- Is it worth investing in premium automotive seating with built-in electronics?
- How does modular seating compare to fixed seating in vehicles?
- How can advanced E-Systems improve vehicle passenger safety?
- How to diagnose issues with in-car electronic power management systems
- What are the best advanced automotive seating solutions for electric vehicles?
- Automotive E-Systems versus traditional wiring harnesses — which is better for new car models?
- What are the options for basic automotive electronic systems for entry-level vehicles?
- Are advanced in-vehicle electronic technologies necessary for mid-range vehicles?
- Low-cost substitutes for advanced automotive seating in commercial vehicles
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/lear.com"><img src="https://aeo-audit.rezolve.com/us/badges/lear.com.svg" alt="Overall score 27 of 100, US AI Visibility Study 2026" width="280" height="64"></a>