- Visibility17
- Citations0
- Sentiment60
- Site quality87
- AI readiness43
AI assistants name Haemonetics in 6.7% of answers, which is more often than the typical company in the healthcare sector. Its overall score is 36 out of 100, ranking 771st of 2,047 companies. The website passes 6 of 14 technical checks for AI tool readiness.
We asked 15 unbranded questions about the healthcare market: none named Haemonetics. AI assistants named it in 4 of 60 answers (6.7%). The typical healthcare company is named in 3.3% of answers. By how often it is named, Haemonetics ranks 40 of 105 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.
105 companies in this sector. The line on each bar is the typical company.
An answer that names the company
“Are cutting-edge plasma center management platforms a cost-effective improvement over standard solutions?”
Short answer: Often yes — but “cutting‑edge” platforms are cost‑effective only when the center’s scale, complexity, or strategic goals make their benefits (higher throughput, fewer errors, better donor retention, regulatory support, analytics) outweigh the higher purchase, integration, validation, and ongoing costs. Whether they’re worth it for a specific center depends on volume, current process pain points, and how you measure ROI. (fda.gov)
Why they can be cost‑effective (benefits)
Higher throughput and less staff …
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%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 43 of 100 · 97% of checks could run · checked on October 5, 2026 · www.haemonetics.com · 2,047 companies scanned
6 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%
- 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*allowed
- ClaudeBot*allowed
- Anthropic-AI*allowed
- Google-Extended*allowed
- Applebot-Extendedallowed
- Meta-ExternalAgentallowed
- FacebookBot*allowed
- Bytespiderallowed
- CCBotallowed
- Diffbotallowed
- Omgilibotallowed
- OAI-SearchBotallowed
- Claude-SearchBotallowed
- PerplexityBot*allowed
- Applebotallowed
- Amazonbotallowed
- YouBotallowed
- ChatGPT-User*allowed
- 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.
- Plasma collection technology vs traditional donation methods: which is more effective for centers?
- Alternatives to workflow optimization software for midsize plasma centers
- How can hospitals use medical technology to streamline patient blood management programs?
- What are the most effective medical technology solutions for improving care quality in hospitals?
- What options exist besides integrated transfusion management systems for improving care quality in hospitals?
- What factors influence the cost of implementing automated blood collection technology in the US?
- Common challenges faced by hospitals when integrating new medical technology solutions
- How should blood centers address data migration issues during implementation of new management platforms?
- What medical tech options can help blood centers drive greater efficiency?
- Are cutting-edge plasma center management platforms a cost-effective improvement over standard solutions?
- Best innovations for enhancing operational efficiency at plasma donation centers
- Typical price range for medical technology platforms designed for blood center efficiency
- Is investing in advanced hospital transfusion management systems worth the cost for mid-sized healthcare facilities?
- Use cases for software that optimizes plasma supply chain operations
- How do automated blood collection systems compare to manual processes in terms of safety and efficiency?
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/haemonetics.com"><img src="https://aeo-audit.rezolve.com/us/badges/haemonetics.com.svg" alt="Overall score 36 of 100, US AI Visibility Study 2026" width="280" height="64"></a>