- Visibility21
- Citations0
- Sentiment68
- Site quality80
- AI readiness21
AI assistants name Arrow Electronics in 18.3% of answers, which is more often than the typical company in its sector. Its overall score is 35 out of 100, ranking 816th of 2,047 in the study. The website passes 3 out of 14 technical checks for readiness with AI tools.
We asked 15 unbranded questions about the technology market: none named Arrow Electronics. AI assistants named it in 11 of 60 answers (18.3%). The typical technology company is named in 10.0% of answers. By how often it is named, Arrow Electronics ranks 90 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
“How are bulk discounts typically structured when buying electronic parts in the US?”
I can provide you with general information about bulk discounts for electronic parts in the US based on common industry practices.
Bulk discounts for electronic parts are typically structured in several ways:
1. Tiered Pricing - This is the most common approach. Suppliers offer different price points based on order quantity ranges. For example, prices might be lower per unit when ordering 100+ units, even lower at 500+ units, and so on. As the quantity increases, the per-unit cost decreases.
2. Volume Discounts - Percentage discounts off the list price that increase with order volum …
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 headersCould not check
Send Link headers such as rel="api-catalog" or rel="describedby" on the homepage.
- study: 5.5%DNS for AI DiscoveryPassed
SVCB or HTTPS records under _agents that advertise agent endpoints.
- study: 1.9%Markdown negotiationCould not check
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 33 of 100 · 81% of checks could run · checked on October 3, 2026 · www.arrow.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%
- Not metToken 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 7 AI crawlers by name. All others follow its general rules.
- GPTBot*partly
- ClaudeBot*partly
- Anthropic-AI*partly
- Google-Extended*partly
- Applebot-Extendedpartly
- Meta-ExternalAgentpartly
- FacebookBotpartly
- Bytespiderpartly
- CCBot*partly
- Diffbotpartly
- Omgilibotpartly
- OAI-SearchBotpartly
- Claude-SearchBotpartly
- PerplexityBot*partly
- Applebotpartly
- Amazonbotpartly
- YouBotpartly
- ChatGPT-User*partly
- 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 US-listed.
- How are bulk discounts typically structured when buying electronic parts in the US?
- What factors influence lead times and prices for custom electronic solutions?
- How do technology solutions providers help streamline the design-to-manufacturing process?
- Where can US businesses find direct-to-manufacturer sourcing options for electronic parts?
- What are the main differences between global and regional electronic components providers?
- Is it worth partnering with an engineering services provider for product development?
- Are all-in-one technology solutions better for scaling electronics projects, or should I use separate vendors?
- What are the best electronic component distributors for small businesses in the US?
- How can businesses resolve mismatches or shortages when sourcing electronic components online?
- How does sourcing from online electronic component distributors compare to working with local suppliers?
- What features should I look for in a technology solutions partner for new electronics development?
- What are alternatives to using full-service electronic component distributors for engineering teams?
- Top technology solutions providers for hardware startups looking to innovate
- What services do electronic component distributors typically offer to support complex IoT projects?
- Why are my BOM orders getting delayed through US electronic parts 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/arrow.com"><img src="https://aeo-audit.rezolve.com/us/badges/arrow.com.svg" alt="Overall score 35 of 100, US AI Visibility Study 2026" width="280" height="64"></a>