- Visibility15
- Citations1
- Sentiment89
- Site quality80
- AI readiness21
AI assistants name Avnet in 15% of answers, more often than the typical company in its sector at 10%. Its overall score is 38 out of 100, ranking 549th of 2,047. The website passes 3 out of 14 technical checks for readiness for AI tools.
We asked 15 unbranded questions about the technology market: none named Avnet. AI assistants named it in 9 of 60 answers (15.0%). The typical technology company is named in 10.0% of answers. By how often it is named, Avnet ranks 104 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
“Where do US-based electronics startups typically find support for component selection and sourcing?”
US-based electronics startups can find support for component selection and sourcing through various channels, including hardware accelerators, specialized distributors, and professional sourcing services.
Key resources and strategies include:
Hardware Accelerators and Incubators Many accelerators focus specifically on hardware and can provide crucial guidance, mentorship, and resources for component selection and sourcing.
HAX and Highway1 are frequently recommended for their focus on hardware startups and their ability to provide valuable resources and mentorship. …
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
The website turns away requests that do not come from a browser. AI agents that say what they are meet the same refusal.
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 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 25 of 100 · 97% of checks could run · checked on October 3, 2026 · avnet.com · 2,047 companies scanned
3 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%
- 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%
- 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 US-listed.
- What are some other ways to access technical expertise for electronics design besides going through a distributor?
- How can electronics manufacturers leverage distributor technical services for IoT product development?
- Where do US-based electronics startups typically find support for component selection and sourcing?
- When should design engineers consult with a distributor's field applications engineer during a project?
- What to do if your shipment of electronic components is delayed or incomplete?
- Typical pricing tiers for high-volume orders of semiconductor components in the US
- How do online versus local electronic component distributors compare for lead times?
- What are the best electronic component distributors for rapid prototyping needs?
- Alternatives to using traditional electronic component distribution channels for prototype sourcing
- Common issues with product lifecycle support from electronic component distributors and how to resolve them
- Best places to source high-quality microcontrollers for industrial applications in the US
- Do electronic component distributors offer better bulk pricing compared to manufacturer-direct orders?
- Is it worth partnering with an electronic components distributor for custom sourcing solutions?
- Technical services from electronic component distributors vs direct from manufacturers: Which is better for product development?
- How do pricing structures differ between small-batch and production-scale purchases at electronic component distributors?
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/avnet.com"><img src="https://aeo-audit.rezolve.com/us/badges/avnet.com.svg" alt="Overall score 38 of 100, US AI Visibility Study 2026" width="280" height="64"></a>