- Visibility35
- Citations8
- Sentiment74
- Site quality73
- AI readiness36
AI assistants name Synopsys in 38.3% of answers, which is much more often than the typical company in its sector at 10%. Its overall score is 43 out of 100, ranking 169th of 2,047 companies. The company's website passes 5 of 14 technical checks for readiness with AI tools.
We asked 15 unbranded questions about the technology market: none named Synopsys. AI assistants named it in 23 of 60 answers (38.3%). The typical technology company is named in 10.0% of answers. By how often it is named, Synopsys ranks 34 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 EDA workflows are recommended for system-level design and verification?”
For system-level design and verification, several Electronic Design Automation (EDA) workflows are recommended to manage the complexity of modern electronic systems and ensure successful implementation. These workflows often involve a combination of design, simulation, and verification tools, with an increasing emphasis on higher levels of abstraction and automation.
Here's a breakdown of recommended EDA workflows:
1. High-Level Design and Abstraction (Electronic System Level - ESL):
Modeling the entire system: This involves using high-level languages like C, C++, or graphical mo …
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 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.mdCould not check
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 36 of 100 · 98% of checks could run · checked on October 3, 2026 · www.synopsys.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%
- PassedHTTP 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.
- What EDA workflows are recommended for system-level design and verification?
- Is it better to license semiconductor IP or build in-house for a new chip design?
- What factors influence the pricing of advanced EDA tool subscriptions for US companies?
- Why do timing closure issues occur during silicon verification, and how can EDA tools help?
- How much should semiconductor firms budget annually for comprehensive EDA software and IP licensing?
- How can hardware-assisted verification speed up AI chip development?
- What are the best EDA platforms for accelerating silicon design workflows?
- Are commercial EDA solutions worth the investment for small semiconductor startups?
- Which electronic design automation tools are popular for advanced chip development in the US?
- How do semiconductor IP libraries compare to custom IP development for chip designers?
- What are alternatives to traditional EDA software for small-scale electronic system development?
- EDA tools vs open-source hardware design suites: which is better for complex semiconductor projects?
- What features should US chip designers look for when selecting an EDA solution?
- Which cloud-based EDA tools are viable alternatives for remote semiconductor teams?
- What are common challenges with multi-die design signoff in modern EDA environments?
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/synopsys.com"><img src="https://aeo-audit.rezolve.com/us/badges/synopsys.com.svg" alt="Overall score 43 of 100, US AI Visibility Study 2026" width="280" height="64"></a>