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.

SWIFT47/60TRANSUNION46/60EXPERIAN42/60COINSTAR41/60NAVY FEDERAL CREDIT UNION41/60ZELLE41/60BLOOMBERG L.P.38/60TRADINGVIEW38/60BANCO POPULAR DE PUERTO RICO37/60PAYPAL37/60VENMO37/60CHEXSYSTEMS33/60EQUIFAX33/60MORNINGSTAR32/60S&P GLOBAL31/60ALPACA MARKETS30/60COINBASE30/60PINWHEEL29/60STRIPE29/60XERO29/60ACORNS28/60AFFIRM28/60PAYFAST (PAYGATE)28/60PAYONEER28/60INTERACTIVE BROKERS26/60CHARLES SCHWAB19/60THE HARTFORD9/60MOODY'S21/60CME GROUP19/60ACCENTURE11/60CHUBB10/60MORGAN STANLEY6/60AMERICAN EXPRESS14/60SYNCHRONY FINANCIAL12/60FACTSET20/60NASDAQ19/60ALLSTATE11/60WELLS FARGO18/60PAYCHEX10/60ARES MANAGEMENT3/60
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MethodDemo
Back to the study
AI visibility statementFinance AI Visibility Study 2026Issued October 14, 2026
Account holder

Datasharp

datasharp.com

Market data, ratings and exchangesFinancial technology and data

Money20/20 attendee

Statement period
October 9 to 14, 2026
when the AI assistants were asked
Account no.
1,774 of 2,219
its rank by overall score
Place in its category
36th of 37 market data companies
Closing balance

Named in

0of 60, its tower is dark

by 0 of 4 assistants

Statement note

What this means for Datasharp

AI assistants do not name Datasharp in any of the 60 answers to market questions, while they name the typical company in its sector in 1.7% of answers. Datasharp has an overall score of 25 out of 100, ranking 1,774th of 2,219 companies. Its website passes 1 of 14 technical checks for readiness for AI tools.

We asked 15 unbranded questions about its market, so no question mentioned Datasharp by name. AI assistants named it in none of the 60 answers. Counting only how often it is named, it ranks 334th of 574 in its sector. The typical financial technology and data company 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
25Overall score
  • Visibility0
  • Citations0
  • Sentiment50
  • Site quality87
  • AI readiness7
Summary of account
Named by AI in
0.0%
0 of 60 answers
Share of mentions
0.0%
no answer named it or its 5 peers
AI readiness
1/14
technical checks passed. Typical for its sector: 4
Rank in its sector
486 of 574
by overall score, where the typical financial technology and data company scores 29

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Line itemsSection 01

Every question, and where the company came up

Datasharp came up on 0 of 15 questions. Open any window to read that answer.

  1. 01Is it worth seeking acquisition if you run a small data-focused company?0/4
  2. 02What fees or costs should company data providers expect during the acquisition process?0/4
  3. 03Other ways to achieve international growth for data businesses without being acquired0/4
  4. 04Alternatives to selling outright for owners of specialist data companies0/4
  5. 05What should specialist data company owners know before seeking investment or acquisition in the US?0/4
  6. 06How can company data providers maintain operational independence after acquisition?0/4
  7. 07What challenges can arise when integrating a data business into a larger group?0/4
  8. 08What are the most important factors to consider when selling a specialist data business?0/4
  9. 09What are the differences between holding companies and venture capital firms for specialist data providers?0/4
  10. 10How do specialist data businesses benefit from joining a group of similar companies?0/4
  11. 11Pros and cons of investment versus acquisition for niche data businesses0/4
  12. 12Best ways to expand the reach of a company data provider in the US market0/4
  13. 13How are sale prices typically determined for specialist data businesses in the US?0/4
  14. 14Common reasons data companies look for investment partners0/4
  15. 15How can a specialist data business increase its impact by partnering with a holding group?0/4
Total namedOpenAI: 0/15Claude: 0/15Perplexity: 0/15Gemini: 0/15All assistants: 0/60
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.

PayeesSection 02

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. Datasharp
    0.0%
  2. enpaas
    0.0%
  3. Bowmark Capital
    0.0%
  4. HG Capital
    0.0%
  5. RELX Ventures
    0.0%
  6. Synova
    0.0%
ReferrersSection 03

Websites the AI used as sources

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

  1. 01
    ctacquisitions.com
    6
  2. 02
    websiteclosers.com
    4
  3. 03
    linkedin.com
    4
  4. 04
    bdo.com
    3
  5. 05
    grata.com
    3
  6. 06
    dhl.com
    3
  7. 07
    startupfundraising.com
    3
  8. 08
    aventis-advisors.com
    2
  9. 09
    sharevault.com
    2
  10. 10
    blog.acquire.com
    2

Datasharp is in the study as a market data company.

By assistantSection 04

Which AI assistants named the company

Answers naming it, out of answers received

Claude
0/15
Gemini
0/15
OpenAI
0/15
Perplexity
0/15
Market comparisonSection 05

How Datasharp 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
0

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

Citations
0

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
7

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 06

Compared with financial technology and data companies

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

Overall score
25
Ranks 486 of 574. Typical company: 29
Named in answers
0.0%
Ranks 334 of 574. Typical company: 1.7%
AI readiness
7
Ranks 533 of 574. Typical company: 29
Against its categorySection 07

Compared with market data companies

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

Overall score
25
Ranks 35 of 37. Typical company: 36
Named in answers
0.0%
Ranks 30 of 37. Typical company: 15.0%
AI readiness
7
Ranks 35 of 37. Typical company: 29
AI agentsSection 08

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 0: Not yet agent-ready. Agents struggle to find basic information about the site.

To reach level 1:
  • 2 of robots.txt, Sitemap, Link headers
Can an AI agent check out here?

Not by itself. Datasharp 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.txtNot present
  • WebMCPCould not check
Show every check, for your technical team
Discoverability
0 of 100
  • robots.txtNot met

    Publish a valid robots.txt with crawl rules and a Sitemap line.

    study: 86.1%
  • SitemapNot met

    Serve an XML sitemap and reference it 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
0 of 100
  • Markdown negotiationNot met

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

    study: 6.2%
  • llms.txtNot met

    Publish an llms.txt with a short summary and the key links.

    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 7 of 100 · 97% of checks could run · checked on October 9, 2026 · datasharp.com · 2,200 companies scanned

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

AI readinessSection 09

The checks behind the AI readiness score

1 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

  • Not metrobots.txt publishedstudy: 85.1%
  • Not metXML sitemapstudy: 79.4%
  • Not metHTTP Link headers (RFC 8288)study: 42.5%

Content Accessibility

  • Not metMarkdown content negotiationstudy: 7.4%
  • Not metllms.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%
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End of statement
All 2,219 companiesHow we measured this
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