- Visibility15
- Citations1
- Sentiment50
- Site quality87
- AI readiness29
AI assistants name Dana in 5% of their answers, which matches the typical company in its sector. Dana's overall score is 33 out of 100, ranking 996th of 2,047 companies. Its website passes 4 out of 14 technical checks for how ready it is for AI tools.
We asked 15 unbranded questions about the automotive market: none named Dana. AI assistants named it in 3 of 60 answers (5.0%). The typical automotive company is named in 5.0% of answers. By how often it is named, Dana ranks 21 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
“Are premium drivetrain components justified for linehaul operations?”
Yes—premium drivetrain components are often justified in linehaul operations, but mainly when the fleet is high-mileage, fuel-focused, and built for long highway duty cycles. The strongest support for premium spec’ing is that linehaul trucks can benefit from components optimized for fuel economy, reduced maintenance, and durability, and downspeeding/efficiency-oriented setups are specifically recommended for true linehaul use.
Key reasons they can pay off:
Fuel economy gains matter most in linehaul because trucks spend much of their time at steady road speed; sources …
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%SitemapPartly 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 discoveryPassed
OpenID Connect or OAuth metadata agents can use to sign in.
- 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%WebMCPNot met
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 26 of 100 · 100% of checks could run · checked on October 3, 2026 · www.dana.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%
- 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%
- PassedOAuth 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.
- Common reasons for reduced efficiency in e-Propulsion systems?
- Innovative drivetrain technologies transforming mobility in the US transportation sector?
- What are the pros and cons of e-Propulsion versus conventional propulsion in vocational vehicles?
- Are premium drivetrain components justified for linehaul operations?
- Alternative propulsion systems for fleets not ready to transition to full electric?
- What are the best drivetrain systems for commercial electric vehicles?
- Which propulsion systems are best for specialty vehicles used in defense operations?
- What types of drivetrain systems are suited for SUV and CUV applications?
- What are the main alternatives to fully electrified drivetrains for reducing emissions?
- What factors influence the total cost of ownership for commercial vehicle e-Propulsion solutions?
- Which e-Propulsion solutions are most reliable for heavy-haul trucks?
- Is upgrading to an electrified drivetrain system worth the investment for medium-duty fleets?
- How can manufacturers address vibrations in high-performance vehicle drivetrains?
- How do prices of electric versus traditional drivetrains compare in the US market?
- How does a traditional drivetrain system differ from a hybrid drivetrain system?
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/dana.com"><img src="https://aeo-audit.rezolve.com/us/badges/dana.com.svg" alt="Overall score 33 of 100, US AI Visibility Study 2026" width="280" height="64"></a>