Of 60 answers

The most named companies in the study, and S&P 500 financials, with how many of their 60 AI answers named them.

Back to the study
AI visibility statementFinance AI Visibility Study 2026Issued October 14, 2026
Account holder
Statement period
October 9 and 10, 2026
when the AI assistants were asked
Account no.
846 of 2,219
its rank by overall score
Place in its category
7th of 18 insurtech companies
Closing balance

Named in

1of 60 answers

by 1 of 4 assistants

Statement note

What this means for Sure

AI assistants rarely name this insurtech company in their answers, mentioning it in 1 out of 60 answers. Its overall score is 31 out of 100, ranking 846th of 2,219 companies. The website passes 5 of 14 technical checks for readiness for AI tools.

We asked 15 unbranded questions about its market, so no question mentioned Sure by name. AI assistants named it in 1 of the 60 answers. Counting only how often it is named, it ranks 41st of 97 in its sector. The typical insurer is named in 1.7% of answers.

Named fewer than three times in total. Read this as a sign that AI assistants rarely bring this company up, not as an exact comparison with its peers.

Overall score
31Overall score
  • Visibility7
  • Citations1
  • Sentiment50
  • Site quality87
  • AI readiness36
Summary of account
Named by AI in
1.7%
1 of 60 answers
Share of mentions
50.0%
of the mentions of it and its 5 peers
AI readiness
5/14
technical checks passed. Typical for its sector: 3
Rank in its sector
33 of 97
by overall score, where the typical insurer scores 28

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Line itemsSection 01

Every question, and where the company came up

Sure came up on 1 of 15 questions. Open any window to read that answer.

  1. 01How can data privacy concerns be addressed when using AI-driven insurance infrastructure for customer policy servicing?0/4
  2. 02How do agentic insurance infrastructure platforms differ from basic insurtech solutions in terms of automation and compliance?0/4
  3. 03Are there open-source solutions suitable for building custom insurance digital platforms at scale?0/4
  4. 04What is the best way for enterprises to build whitelabeled digital insurance solutions quickly?0/4
  5. 05What pricing models are typical for enterprise-grade AI insurance platforms in the US?0/4
  6. 06What types of modular insurance technology can brands use instead of complete end-to-end platforms?1/4
  7. 07What are some innovative ways enterprises are embedding insurance offerings into digital customer journeys?0/4
  8. 08AI insurance platforms vs traditional policy administration systems—what are the major differences?0/4
  9. 09How effective are digital insurance infrastructure platforms for enterprises seeking to modernize legacy insurance processes?0/4
  10. 10Is investing in an AI-powered insurance platform worthwhile for brands looking to offer embedded insurance products?0/4
  11. 11What alternatives exist to adopting a full-stack AI insurance infrastructure if a brand only needs enrollment and policy servicing tools?0/4
  12. 12How much should a large enterprise expect to budget for a compliant, whitelabeled insurance infrastructure platform per year?0/4
  13. 13How can large brands use AI-enabled platforms to streamline underwriting and claims processes for their insurance programs?0/4
  14. 14What are common challenges when integrating a no-code insurance platform into existing brand ecosystems?0/4
  15. 15What are the most important features to look for in an AI-powered insurance infrastructure platform for launching custom programs?0/4
Total namedOpenAI: 0/15Claude: 1/15Perplexity: 0/15Gemini: 0/15All assistants: 1/60
named the companyanswered without naming itdid not answer

Columns, left to right: OpenAI, Claude, Perplexity, Gemini. Across the study, on 50% of the questions where a company is named, only one AI assistant names it.

Transaction detailSection 02

What Claude actually said

An answer that names the company

Claudenamed the company

“What types of modular insurance technology can brands use instead of complete end-to-end platforms?”

… Sure is an insurtech company that offers services including policy management, claims processing, and insurance distribution, serving clients including insurance carriers, brands, marketplaces, SaaS providers, and managing general agents.

These modular approaches allow brands to select and combine only the specific components they need rather than implementing a monolithic end-to-end system.

The AI’s answer as we received it, shortened and not checked for accuracy.

PayeesSection 03

Where the mentions went

The companies these answers named, and how the mentions were split between this company and its peers.

Share of mentions

CompanyShare
  1. Sure
    50.0%
  2. Cover Genius
    50.0%
  3. Boost Insurance
    0.0%
  4. Socotra
    0.0%
  5. Trov
    0.0%
  6. Slice Labs
    0.0%
ReferrersSection 04

Websites the AI used as sources

How many times each website was listed as a source in these answers

  1. 01
    eisgroup.com
    8
  2. 02
    chubb.com
    6
  3. 03
    furtherai.com
    5
  4. 04
    linkedin.com
    5
  5. 05
    sapiens.com
    5
  6. 06
    assets.kpmg.com
    5
  7. 07
    qover.com
    5
  8. 08
    damcogroup.com
    4
  9. 09
    infosys.com
    4
  10. 10
    genevaassociation.org
    4

Sure is in the study as an insurtech company.

By assistantSection 05

Which AI assistants named the company

Answers naming it, out of answers received

Claude
1/15
Gemini
0/15
OpenAI
0/15
Perplexity
0/15
Market comparisonSection 06

How Sure compares with all 2,219 companies

Every tower is a company in the study, taller for a higher overall score and lit where AI assistants named it. Its own group stays lit. Hover a tower to see it, and click to open it.

What makes up the score

Each part is scored out of 100.

Visibility
7

How often AI assistants name the company, and how that compares with its peers

Citations
1

How often AI uses the company's own website as a source

Sentiment
50

How positively AI describes the company

Site quality
87

How well built the website's own pages are

AI readiness
36

How easily AI tools can read and use the website

Visibility is calculated from the answers on this page. How the score is built.

Against its sectorSection 07

Compared with insurers

97 companies in this sector. The gold tick on each bar is the typical company.

Overall score
31
Ranks 33 of 97. Typical company: 28
Named in answers
1.7%
Ranks 41 of 97. Typical company: 1.7%
AI readiness
36
Ranks 7 of 97. Typical company: 29
Against its categorySection 08

Compared with insurtech companies

18 companies in this category. The gold tick on each bar is the typical company.

Overall score
31
Ranks 7 of 18. Typical company: 29
Named in answers
1.7%
Ranks 7 of 18. Typical company: 1.7%
AI readiness
36
Ranks 3 of 18. Typical company: 33
AI agentsSection 09

Ready for AI agents

What this website publishes for an AI agent to read, use and pay through, on a scale of six levels.

  1. 0
  2. 1
  3. 2
  4. 3
  5. 4
  6. 5

Level 1: Basic web presence. Two of robots.txt, a sitemap and Link headers are in place.

To reach level 2:
  • Content Signals
Can an AI agent check out here?

Not by itself. Sure publishes none of the agent payment protocols we look for. An agent would have to click through the website like a person.

Agent payment protocols
  • x402Not present
  • MPPNot present
  • AP2Not present
  • ACPNot present
  • UCPNot present
Interfaces for AI agents
  • MCP Server CardNot present
  • A2A Agent CardNot present
  • Agent SkillsNot present
  • API CatalogNot present
  • llms.txtPublished
  • WebMCPCould not check
Show every check, for your technical team
Discoverability
77 of 100
  • robots.txtPassed

    A valid robots.txt with crawl rules at the site root.

    study: 86.1%
  • SitemapPassed

    An XML sitemap listing the pages, ideally referenced from robots.txt.

    study: 87.9%
  • Link headersNot met

    Send Link headers such as rel="api-catalog" or rel="describedby" on the homepage.

    study: 1.5%
  • DNS for AI DiscoveryNot present

    Advertise agent endpoints with SVCB records under _agents.

    study: 4.3%
Content accessibility
50 of 100
  • Markdown negotiationNot met

    Return a markdown version when asked with Accept: text/markdown, for example at the CDN.

    study: 6.2%
  • llms.txtPassed

    An llms.txt that tells language models what the site offers.

    study: 31.0%
Bot access control
32 of 100
  • AI bot rulesPartly met

    Add explicit robots.txt groups for the main AI crawlers.

    study: 92.7%
  • Content SignalsNot met

    Add a Content-Signal line for search, ai-input and ai-train to robots.txt.

    study: 2.3%
  • Web Bot AuthNot present

    Only relevant if you run your own agents or crawlers: publish a signing key directory.

    study: 0.3%
APIs, auth and MCP
0 of 100
  • API CatalogNot met

    List public APIs in a linkset at /.well-known/api-catalog.

    study: 1.3%
  • OAuth discoveryNot met

    Publish OpenID Connect or OAuth server metadata under /.well-known.

    study: 6.3%
  • OAuth Protected ResourceNot met

    Serve protected resource metadata at /.well-known/oauth-protected-resource.

    study: 5.3%
  • 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: 0.8%
  • 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: 1.0%
  • WebMCPCould not check

    Register key actions such as search or cart as WebMCP tools.

    study: 0.0%
  • ARD manifestNot met

    Publish an ai-catalog.json listing every agent interface.

    study: 0.2%

Score 36 of 100 · 97% of checks could run · checked on October 9, 2026 · www.sureapp.com · 2,200 companies scanned

See how every company did, and how this was measured.

AI readinessSection 10

The checks behind the AI readiness score

5 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 checks

Discoverability

  • Passedrobots.txt publishedstudy: 85.1%
  • PassedXML sitemapstudy: 79.4%
  • PassedHTTP Link headers (RFC 8288)study: 42.5%

Content Accessibility

  • Not metMarkdown content negotiationstudy: 7.4%
  • Passedllms.txt publishedstudy: 28.1%
  • PassedToken budget (page weight)study: 97.3%

Bot Access Control

  • Not metExplicit AI bot rulesstudy: 11.5%

API / Auth / MCP

  • Not metAPI Catalog (.well-known/api-catalog)study: 1.1%
  • Not metOAuth Authorization Server discoverystudy: 5.2%
  • Not metOAuth Protected Resource discoverystudy: 4.6%
  • Not metMCP Server Cardstudy: 0.2%
  • Not metA2A Agent Cardstudy: 0.0%
  • Not metWebMCP toolsstudy: 0.3%
  • Not metAgent Skills declaredstudy: 0.1%
AI crawlersSection 11

What AI crawlers may read (robots.txt)

This website sets no rules for any AI crawler by name, like 86% of the websites whose rules we could read. Every AI crawler follows its general rules.

Training AI

  • GPTBotpartly
  • ClaudeBotpartly
  • Anthropic-AIpartly
  • Google-Extendedpartly
  • Applebot-Extendedpartly
  • Meta-ExternalAgentpartly
  • FacebookBotpartly
  • Bytespiderpartly
  • CCBotpartly
  • Diffbotpartly
  • Omgilibotpartly

AI search

  • OAI-SearchBotpartly
  • Claude-SearchBotpartly
  • PerplexityBotpartly
  • Applebotpartly
  • Amazonbotpartly
  • YouBotpartly

Answering questions

  • 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.

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