- Visibility20
- Citations3
- Sentiment50
- Site quality87
- AI readiness21
AI assistants name Verisk Analytics in 16.7% of answers, which is more often than the typical company in its sector at 5%. Its overall score is 34 out of 100, ranking 892nd of 2,047. The website passes 3 out of 14 technical checks that show how ready it is for AI tools.
We asked 15 unbranded questions about the industrial market: none named Verisk Analytics. AI assistants named it in 10 of 60 answers (16.7%). The typical industrial company is named in 5.0% of answers. By how often it is named, Verisk Analytics ranks 42 of 246 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.
246 companies in this sector. The line on each bar is the typical company.
An answer that names the company
“What is the typical cost range for cloud-based insurance risk analytics platforms in the US?”
I'll search for current pricing information on cloud-based insurance risk analytics platforms.Based on the search results, here's what I found about pricing for cloud-based insurance risk analytics platforms in the US:
The cost can range from $10,000 to $100,000 for a one-time implementation fee. Additionally, there may be ongoing maintenance and support fees that can range from $10,000 to $50,000 per year.
For enterprise-level risk scoring and underwriting data solutions, the costs are significantly higher. LexisNexis Risk Solutions and Verisk charge $400K to $4.8M annually per Tier 2 carri …
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.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 5, 2026 · www.verisk.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.
- 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 S&P 500.
- Top solutions for streamlining claims management processes in insurance organizations
- What is the typical cost range for cloud-based insurance risk analytics platforms in the US?
- Are advanced risk data analytics platforms worth the investment for midsize insurance companies?
- Are there transparent pricing models for technology solutions in insurance risk assessment?
- Pros and cons of predictive analytics vs. rules-based systems for insurance claims analysis
- Recommended insurance analytics tools tailored for life insurance underwriting in the United States
- Best practices for using data-driven insights to price property and casualty insurance risk
- Open-source or third-party solutions for insurance claims data analytics
- Alternatives to proprietary data-driven risk models for smaller insurance organizations
- Why might insurance risk scores be inconsistent across different analytics systems?
- How do catastrophe modeling solutions compare to traditional risk assessment tools in insurance?
- How to address data integration issues with insurance technology platforms
- How do I evaluate if enterprise-level insurance analytics solutions are necessary for my agency?
- How can insurers leverage analytics to improve fraud detection in claims processing?
- What are the best insurance risk analytics platforms for smarter underwriting decisions?
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/verisk.com"><img src="https://aeo-audit.rezolve.com/us/badges/verisk.com.svg" alt="Overall score 34 of 100, US AI Visibility Study 2026" width="280" height="64"></a>