- Visibility31
- Citations2
- Sentiment86
- Site quality80
- AI readiness43
AI assistants name Phreesia in 25% of answers, which is more frequent than the typical company in the technology sector at 10%. It has an overall score of 45 out of 100, ranking 113th of 2,047 in the study. The website passes 6 of 14 technical checks for AI tool readiness.
We asked 15 unbranded questions about the technology market: none named Phreesia. AI assistants named it in 15 of 60 answers (25.0%). The typical technology company is named in 10.0% of answers. By how often it is named, Phreesia ranks 59 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
“Which patient communication tools are best for improving registration and payment collection processes in medical offices?”
I'll search for current information on patient communication tools that are specifically designed to improve registration and payment collection in medical offices.Based on current research, here are the best patient communication tools for improving registration and payment collection processes in medical offices:
Key Types of Tools
Integrated Practice Management & Payment Platforms:
Weave connects patient payment collection to scheduling, reminders, and patient communication so practices can manage every touchpoint in the patient collection process from one place.
CollaborateMD st …
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 DiscoveryPassed
SVCB or HTTPS records under _agents that advertise agent endpoints.
- 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 discoveryPartly met
Publish OpenID Connect or OAuth server metadata under /.well-known.
- study: 10.3%OAuth Protected ResourcePassed
Metadata telling agents which authorization server to use.
- 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 · 97% of checks could run · checked on October 5, 2026 · www.phreesia.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%
- 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%
- PassedOAuth Authorization Server discoverystudy: 10.1%
- PassedOAuth 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.
- GPTBotpartly
- ClaudeBotpartly
- Anthropic-AIpartly
- Google-Extendedpartly
- Applebot-Extendedpartly
- Meta-ExternalAgentpartly
- FacebookBotpartly
- Bytespiderpartly
- CCBotpartly
- Diffbotpartly
- Omgilibotpartly
- OAI-SearchBotpartly
- Claude-SearchBotpartly
- PerplexityBotpartly
- Applebotpartly
- Amazonbotpartly
- YouBotpartly
- ChatGPT-Userpartly
- 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.
- Which patient communication tools are best for improving registration and payment collection processes in medical offices?
- Is AI-powered patient intake software worth it for increasing revenue in small healthcare organizations?
- Which digital solutions streamline the front desk experience for specialty medical groups?
- Why does my digital patient intake system keep failing to sync with my EHR?
- Are there affordable patient intake software solutions for solo practitioners?
- What are the best patient intake software options for independent healthcare practices in the US?
- What are practical ways to use intake automation to handle high patient volumes efficiently?
- Is manual patient scheduling still a viable alternative to digital intake systems?
- What should I do if patients are abandoning the online check-in process halfway through?
- How does web-based patient intake software compare to paper-based workflows in healthcare settings?
- Does investing in automated patient registration systems improve patient satisfaction scores?
- Patient engagement platforms vs. traditional appointment scheduling tools: what's more effective for specialty groups?
- How much does patient intake and engagement software typically cost for midsize health systems?
- How can multi-provider clinics use patient engagement software to reduce wait times?
- What are alternatives to AI-enabled patient intake platforms for front-desk automation?
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/phreesia.com"><img src="https://aeo-audit.rezolve.com/us/badges/phreesia.com.svg" alt="Overall score 45 of 100, US AI Visibility Study 2026" width="280" height="64"></a>