- Visibility15
- Citations0
- Sentiment63
- Site quality80
- AI readiness29
AI assistants name Qualcomm in 6.7% of answers, which is less often than the typical company in the technology sector. Its overall score is 33 out of 100, ranking 987th of 2,047 companies. The company's website passes 4 of 14 technical checks for how ready it is for AI tools.
We asked 15 unbranded questions about the technology market: none named Qualcomm. AI assistants named it in 4 of 60 answers (6.7%). The typical technology company is named in 10.0% of answers. By how often it is named, Qualcomm ranks 165 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
“Top high-performance computing platforms for automotive applications”
The top high-performance computing (HPC) platforms for automotive applications today are led by NVIDIA DRIVE, Qualcomm Snapdragon Digital Chassis / Ride, NXP S32 / BlueBox, and Mobileye-based systems, with Huawei, Horizon Robotics, and Aptiv also prominent in the market.
NVIDIA DRIVE Thor / DRIVE AGX Orin: NVIDIA is described as the dominant force in high-end automotive HPC, with DRIVE Thor delivering up to 1,000 INT8 TOPS and 2,000 FP4 TFLOPs, while DRIVE AGX Orin delivers up to 254 TOPS.
Qualcomm Snapdragon Digital Ch …
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.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 36 of 100 · 97% of checks could run · checked on October 5, 2026 · www.qualcomm.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%
- 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.
- 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 S&P 500.
- Common issues when integrating high-performance computing modules into existing mobile devices
- How do licensing models for connectivity solutions impact total cost of ownership for OEMs?
- Low-power wireless technology vs traditional wired connectivity—what are the trade-offs?
- Top high-performance computing platforms for automotive applications
- Why might wireless connectivity in IoT sensors experience frequent dropouts?
- How does on-device AI differ from cloud-based AI in wireless computing systems?
- Are premium edge AI platforms a good value for industrial automation use-cases?
- Alternatives to dedicated edge AI chips for intelligent device processing
- What are some substitutes for high-end automotive connectivity systems?
- What are the best wireless connectivity solutions for enterprise IoT deployments?
- What options exist for businesses seeking energy-efficient wireless networking platforms?
- What is the typical price range for enterprise-grade wireless computing modules in the US?
- Is investing in advanced wireless computing hardware worth it for smart city infrastructure projects?
- How can advanced wireless connectivity improve remote healthcare solutions?
- Best uses for low-power AI-powered processors in wearable technology
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/qualcomm.com"><img src="https://aeo-audit.rezolve.com/us/badges/qualcomm.com.svg" alt="Overall score 33 of 100, US AI Visibility Study 2026" width="280" height="64"></a>