- Visibility16
- Citations1
- Sentiment79
- Site quality80
- AI readiness43
AI assistants name Nano Dimension in 8.3% of answers, slightly less often than the typical company in its sector. Its overall score is 38 out of 100, ranking 580th of 2,047 companies. The company's website passes 6 of 14 technical checks for readiness with AI tools.
We asked 15 unbranded questions about the technology market: none named Nano Dimension. AI assistants named it in 5 of 60 answers (8.3%). The typical technology company is named in 10.0% of answers. By how often it is named, Nano Dimension ranks 151 of 273 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.
273 companies in this sector. The line on each bar is the typical company.
An answer that names the company
“What are the best industrial 3D printing solutions for rapid electronics prototyping?”
For rapid electronics prototyping, industrial 3D printing offers significant advantages such as reduced lead times, lower costs, and greater design flexibility compared to traditional manufacturing methods. The ability to quickly iterate on designs is crucial in the fast-paced electronics industry.
Here are some of the best industrial 3D printing solutions and technologies for rapid electronics prototyping:
1. Technologies for Printing Conductive Traces and Components:
Aerosol Jet Printing: This technology deposits conductive inks onto a substrate with high precision, making it …
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 discoveryPartly met
Publish OpenID Connect or OAuth server metadata under /.well-known.
- study: 10.3%OAuth Protected ResourcePassed
Metadata telling agents which authorization server to use.
- 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 31 of 100 · 97% of checks could run · checked on October 5, 2026 · www.nano-di.com · 2,047 companies scanned
6 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%
- PassedOAuth Authorization Server discoverystudy: 10.1%
- PassedOAuth 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.
- GPTBotallowed
- ClaudeBotallowed
- Anthropic-AIallowed
- Google-Extendedallowed
- Applebot-Extendedallowed
- Meta-ExternalAgentallowed
- FacebookBotallowed
- Bytespiderallowed
- CCBotallowed
- Diffbotallowed
- Omgilibotallowed
- OAI-SearchBotallowed
- Claude-SearchBotallowed
- PerplexityBotallowed
- Applebotallowed
- Amazonbotallowed
- YouBotallowed
- ChatGPT-Userallowed
- Claude-Userallowed
- Perplexity-Userallowed
- DuckAssistBotallowed
- MistralAI-Userallowed
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 should I consider before purchasing a 3D printed electronics manufacturing system?
- Which other digital manufacturing techniques compete with 3D printed electronics for prototyping?
- Which additive manufacturing technologies are most effective for producing multilayer electronic circuits?
- What are the best industrial 3D printing solutions for rapid electronics prototyping?
- What are some typical applications for industrial-scale 3D printed electronics in product design?
- How does the pricing of 3D printed electronics systems compare with conventional PCB production machinery?
- How can additive manufacturing be used to accelerate new electronic device development?
- What affects the cost of industrial additive manufacturing solutions for electronics in the US?
- What are environmentally friendly alternatives to conventional electronics manufacturing processes?
- How does industrial 3D printed electronics compare to traditional PCB manufacturing?
- Are there non-additive methods of rapid electronics production that offer similar design flexibility?
- Is it worth investing in an industrial 3D printer for electronics manufacturing?
- Why might a 3D printed electronics prototype have conductivity issues?
- What are common challenges when producing high-density interconnects with additive manufacturing?
- Direct write electronics vs. subtractive PCB fabrication—what are the pros and cons?
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/nano-di.com"><img src="https://aeo-audit.rezolve.com/us/badges/nano-di.com.svg" alt="Overall score 38 of 100, US AI Visibility Study 2026" width="280" height="64"></a>