- Visibility2
- Citations10
- Sentiment78
- Site quality87
- AI readiness36
AI assistants rarely name Dassault Systemes, doing so in 1 out of 60 answers. Its overall score is 36 out of 100, ranking 785th of 2,047 companies. The website passes 5 of 14 technical checks, which shows partial readiness for AI tools.
We asked 15 unbranded questions about the industrial market: none named Dassault Systemes. AI assistants named it in 1 of 60 answers (1.7%). The typical industrial company is named in 5.0% of answers. By how often it is named, Dassault Systemes ranks 159 of 246 in its sector.
Named fewer than three times in total. Read this as a sign that AI assistants rarely bring this company up, not as an exact comparison with its rivals.
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.
246 companies in this sector. The line on each bar is the typical company.
An answer that names the company
“Typical pricing tiers for PLM software for large organizations in the US?”
Based on current market data, PLM software pricing for large organizations in the US varies significantly depending on the platform and deployment model:
Pricing Models
Most PLM platforms price per user per month, so cost scales with headcount. Common pricing structures include:
Enterprise Licensing (Siemens Teamcenter), which is often tailored to larger organizational structures
Modular Pricing (PTC Windchill), where costs are determined by the specific features or modules a company chooses to implement
Subscription and Value Tiers (used by Dassault Systemes 3DEXPERIENCE and Arena …
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.txtPassed
An llms.txt that tells language models what the site offers.
- study: 93.4%AI bot rulesPassed
Explicit robots.txt rules for AI crawlers such as GPTBot, ClaudeBot and PerplexityBot.
- 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 43 of 100 · 97% of checks could run · checked on October 5, 2026 · www.3ds.com · 2,047 companies scanned
5 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%
- PassedExplicit 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 rules for 9 AI crawlers by name. All others follow its general rules.
- GPTBot*allowed
- ClaudeBot*allowed
- Anthropic-AIpartly
- Google-Extended*allowed
- Applebot-Extended*allowed
- Meta-ExternalAgentpartly
- FacebookBotpartly
- Bytespider*allowed
- CCBot*allowed
- Diffbotpartly
- Omgilibotpartly
- OAI-SearchBotpartly
- Claude-SearchBotpartly
- PerplexityBot*allowed
- Applebotpartly
- Amazonbotpartly
- YouBotpartly
- ChatGPT-User*allowed
- Claude-Userpartly
- Perplexity-Userpartly
- DuckAssistBotpartly
- MistralAI-User*allowed
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.
- Common challenges when integrating PLM software with existing enterprise systems?
- Is investing in a collaborative 3D platform worth it for healthcare organizations?
- What are the best 3D experience platforms for large manufacturers?
- Where can US-based teams find cloud-based PLM solutions for real-time collaboration?
- PLM software vs. 3D experience platforms for infrastructure projects — what's the difference?
- Alternatives to collaborative 3D innovation platforms for product development teams?
- Best product lifecycle management software for enterprise collaboration?
- Ways that virtual twin technology supports sustainable innovation in infrastructure and cities.
- Best virtual twin technology alternatives for infrastructure design projects?
- How does virtual twin technology differ from traditional PLM systems?
- Are virtual twin solutions effective for life sciences companies?
- Typical pricing tiers for PLM software for large organizations in the US?
- Why might 3D modeling platforms cause data compatibility issues for manufacturers?
- How can PLM solutions improve operational resilience in manufacturing?
- What factors influence the cost of enterprise 3D experience platform licenses?
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.