- Visibility37
- Citations5
- Sentiment66
- Site quality93
- AI readiness36
AI assistants name Intel in 45% of their answers, much more often than the typical company in the technology sector at 10%. Intel has an overall score of 45 out of 100, ranking 100th of 2,047 companies in the study. Its website passes 5 of 14 technical checks for readiness with AI tools.
We asked 15 unbranded questions about the technology market: none named Intel. AI assistants named it in 27 of 60 answers (45.0%). The typical technology company is named in 10.0% of answers. By how often it is named, Intel ranks 22 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
“Best computing hardware for running large language models at the edge?”
Running large language models (LLMs) at the edge requires a different approach than cloud-based deployment. The focus shifts from raw tokens per second to optimizing "joules per token" against a fixed thermal design point and power budget.
Here's a breakdown of the best computing hardware for running LLMs at the edge:
1. Hardware Classes Based on Power Budget and Model Size:
Dedicated M.2 Accelerators (e.g., Hailo-10H):
Best for: Sub-5W, battery-constrained products running 1.5–2B parameter models.
Performance: The Hailo-10H can achieve 9.45 tokens/second …
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%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 41 of 100 · 100% of checks could run · checked on October 3, 2026 · www.intel.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 6 AI crawlers by name. All others follow its general rules.
- GPTBot*allowed
- ClaudeBot*allowed
- Anthropic-AI*allowed
- Google-Extended*allowed
- Applebot-Extendedpartly
- Meta-ExternalAgentpartly
- FacebookBotpartly
- Bytespiderpartly
- CCBotpartly
- Diffbotpartly
- Omgilibotpartly
- OAI-SearchBotpartly
- Claude-SearchBotpartly
- PerplexityBot*allowed
- Applebotpartly
- Amazonbotpartly
- YouBotpartly
- ChatGPT-User*allowed
- 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.
- How much should enterprises expect to spend on scalable AI infrastructure for data centers?
- Best computing hardware for running large language models at the edge?
- Graphics accelerators vs. AI accelerators for scaling machine learning workloads in the cloud—pros and cons?
- What are the typical cost differences between cloud-based and on-premises AI computing hardware?
- Are enterprise-grade AI hardware solutions worth the investment for midsize organizations?
- What are the best AI-ready processors for data center workloads in 2024?
- Is it better to upgrade to a new AI-focused processor or wait for the next generation of hardware?
- Recommended hardware options besides mainstream CPUs for boosting AI performance in network applications?
- How can I resolve compatibility issues between AI software frameworks and newer server processors?
- High-density server CPUs vs. general-purpose processors for enterprise AI deployments—what’s the difference?
- Which computing hardware solutions offer top performance for edge AI applications?
- What are the alternatives to x86-based processors for running advanced AI workloads at scale?
- Affordable alternatives to high-end AI accelerators for small business edge deployments?
- Why isn’t my AI model training as fast as expected on my current enterprise hardware setup?
- What solutions provide both high-performance graphics and power-efficient AI upscaling for business laptops?
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/intel.com"><img src="https://aeo-audit.rezolve.com/us/badges/intel.com.svg" alt="Overall score 45 of 100, US AI Visibility Study 2026" width="280" height="64"></a>