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FactSet

factset.com · Finance · FDS

Ranked 372 of 2,047 companies in the study

40Overall score
  • Visibility33
  • Citations4
  • Sentiment79
  • Site quality73
  • AI readiness14
What this means for FactSet

AI assistants name FactSet in 33.3% of answers, much more often than the typical company in its sector, which appears in 3.3% of answers. FactSet has an overall score of 40 out of 100, ranking 372nd of 2,047 companies. Its website passes 2 of 14 technical checks for AI tool readiness.

We asked 15 unbranded questions about the finance market: none named FactSet. AI assistants named it in 20 of 60 answers (33.3%). The typical finance company is named in 3.3% of answers. By how often it is named, FactSet ranks 12 of 205 in its sector.

Named by AI in
33.3%
20 of 60 answers
Share of mentions
32.3%
compared with 5 rivals
AI readiness
2/14
technical checks passed. Typical for its sector: 3
Rank in its sector
27 of 205
by overall score, where the typical finance company scores 29
How FactSet compares with all 2,047 companies

Every dot is a company in the study. Hover one to see it; click to open its report.

This companySame sectorEvery other companyNever named

What makes up the score

Five parts, each scored out of 100.

Visibility
33

How often AI assistants name the company

Citations
4

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

Sentiment
79

How positively AI describes the company

Site quality
73

How well built the website's own pages are

AI readiness
14

How easily AI tools can read and use the website

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

Compared with finance

205 companies in this sector. The line on each bar is the typical company.

Overall score40
Ranks 27 of 205. Typical company: 29
Named in answers33%
Ranks 12 of 205. Typical company: 3%
AI readiness14
Ranks 180 of 205. Typical company: 21
What Gemini actually said

An answer that names the company

“Portfolio analytics platforms vs. investment research tools—what are the key differences and use cases?”

While both portfolio analytics platforms and investment research tools are crucial for informed decision-making in the financial world, they serve distinct purposes and cater to different stages of the investment process. Understanding their key differences and use cases is essential for investors and financial professionals.

Here's a breakdown:

Portfolio Analytics Platforms

Key Focus: Understanding, measuring, and optimizing the performance and risk of existing or proposed portfolios. It's about looking backward at what has happened and forward at how to improve existing …

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

Also named
Bloomberg Terminal9YCharts4S&P Capital IQ1
Share of mentions

How the mentions in these answers were split between this company and its rivals

FactSet
35.1%
Bloomberg Terminal
29.8%
Morningstar Direct
10.5%
S&P Capital IQ
8.8%
YCharts
8.8%
Refinitiv (an LSEG Business)
7.0%
Which AI assistants named the company

Answers naming it, out of answers received

Perplexity
8/15
Claude
6/15
Gemini
4/15
OpenAI
2/15
Ready for AI agents

What this website publishes for an AI agent to read, use and buy from, 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
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: 92.0%
  • SitemapPassed

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

    study: 86.8%
  • Link headersNot met

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

    study: 0.3%
  • DNS for AI DiscoveryNot present

    Advertise agent endpoints with SVCB records under _agents.

    study: 5.5%
Content accessibility
0 of 100
  • Markdown negotiationNot met

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

    study: 1.9%
  • llms.txtNot met

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

    study: 25.1%
Bot access control
32 of 100
  • AI bot rulesPartly met

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

    study: 93.4%
  • Content SignalsNot met

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

    study: 1.4%
  • Web Bot AuthNot present

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

    study: 0.2%
APIs, auth and MCP
0 of 100
  • API CatalogNot met

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

    study: 0.3%
  • OAuth discoveryNot met

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

    study: 11.8%
  • OAuth Protected ResourceNot met

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

    study: 10.3%
  • auth.mdNot met

    Publish an auth.md describing how agents sign in.

    study: 0.0%
  • MCP Server CardNot met

    Offer an MCP server and publish its server card under /.well-known.

    study: 0.2%
  • A2A Agent CardNot met

    Publish an agent card at /.well-known/agent-card.json.

    study: 0.1%
  • Agent SkillsNot met

    Publish a skills index with the main tasks agents can do.

    study: 0.2%
  • WebMCPCould not check

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

    study: 33.7%
  • ARD manifestNot met

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

    study: 0.1%

Score 25 of 100 · 97% of checks could run · checked on October 5, 2026 · www.factset.com · 2,047 companies scanned

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

The checks behind the AI readiness score

2 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: 85.0%
  • PassedXML sitemapstudy: 73.2%
  • Not metHTTP Link headers (RFC 8288)study: 29.0%
Content Accessibility
  • Not metMarkdown content negotiationstudy: 8.3%
  • Not metllms.txt publishedstudy: 21.7%
  • Not metToken budget (page weight)study: 95.2%
Bot Access Control
  • Not metExplicit AI bot rulesstudy: 13.0%
API / Auth / MCP
  • 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%
What AI crawlers may read (robots.txt)

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.

Training AI
  • GPTBotallowed
  • ClaudeBotallowed
  • Anthropic-AIallowed
  • Google-Extendedallowed
  • Applebot-Extendedallowed
  • Meta-ExternalAgentallowed
  • FacebookBotallowed
  • Bytespiderallowed
  • CCBotallowed
  • Diffbotallowed
  • Omgilibotallowed
AI search
  • OAI-SearchBotallowed
  • Claude-SearchBotallowed
  • PerplexityBotallowed
  • Applebotallowed
  • Amazonbotallowed
  • YouBotallowed
Answering questions
  • ChatGPT-Userallowed
  • 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.

Websites the AI used as sources

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

alpha-sense.com
32
factset.com (its own website)
23
hebbia.com
11
koyfin.com
10
bloomberg.com
9
site.financialmodelingprep.com
8
lseg.com
6
brightdata.com
6
tradersmagazine.com
6
g2.com
6

In the study as S&P 500.

Every question, and where the company came up (9 of 15)
QuestionOpenAIClaudePerplexityGemini
  • How can I troubleshoot slow loading of market data dashboards on an integrated analytics platform?
  • Portfolio analytics platforms vs. investment research tools—what are the key differences and use cases?
  • How do institutional investors use market analytics platforms for whole portfolio analysis?
  • Are there affordable financial data platforms that still offer advanced analytics for wealth advisors?
  • How much do AI-powered financial analytics platforms typically cost for small asset management firms?
  • What are common pricing models for premium financial data and market analytics platforms in the US?
  • What are the best financial data platforms for hedge funds looking for portfolio analytics and market insights?
  • What features are essential in financial data solutions for private market insights?
  • Why might financial analytics software generate inaccurate portfolio performance reports?
  • Which financial analytics solutions offer integrated tools useful for wealth advisors in the US?
  • Is a premium financial data and analytics platform worth the investment for asset managers?
  • How does an AI-powered financial analytics platform compare to traditional market data solutions?
  • What alternatives exist to enterprise-level financial analytics platforms for portfolio managers?
  • What should corporations consider when subscribing to a financial data platform with advanced analytics?
  • What are some non-traditional solutions for accessing integrated market data and portfolio analytics in the US?
named the companyanswered without naming itdid not answer

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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