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, 2026
when the AI assistants were asked
Account no.
477 of 2,219
its rank by overall score
Closing balance

Named in

7of 60 answers

by 4 of 4 assistants

Statement note

What this means for People Data Labs

AI assistants name People Data Labs in 11.7% of answers, which is more often than the typical company in its sector at 1.7%. Its overall score is 37 out of 100, ranking 477th 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 People Data Labs by name. AI assistants named it in 7 of the 60 answers. Counting only how often it is named, it ranks 100th of 574 in its sector. The typical financial technology and data company is named in 1.7% of answers.

Overall score
37Overall score
  • Visibility15
  • Citations1
  • Sentiment71
  • Site quality87
  • AI readiness36
Summary of account
Named by AI in
11.7%
7 of 60 answers
Share of mentions
24.1%
of the mentions of it and its 5 peers
AI readiness
5/14
technical checks passed. Typical for its sector: 4
Rank in its sector
152 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

People Data Labs came up on 4 of 15 questions. Open any window to read that answer.

  1. 01What are the best B2B people and company data providers for building HR tech solutions?3/4
  2. 02B2B company data APIs vs traditional prospecting methods—what are the pros and cons?0/4
  3. 03Can I build my own company data enrichment pipeline instead of using a third-party provider?0/4
  4. 04What should I look for when choosing a company data enrichment provider for investment research?0/4
  5. 05How can sales and marketing teams leverage enriched workforce datasets to improve lead generation?0/4
  6. 06How do predictive modeling platforms benefit from standardized people and employment datasets?0/4
  7. 07How does using large-scale people datasets compare to manually sourcing business leads?0/4
  8. 08Is accessing external workforce data worth it for small to midsize recruitment agencies?0/4
  9. 09What is the typical pricing structure for high-volume company and professional data APIs?0/4
  10. 10What are the main ways investment research firms use B2B people data providers?1/4
  11. 11Why does my company data enrichment return incomplete profiles?0/4
  12. 12How do US-based workforce data providers price access to their datasets?1/4
  13. 13Are there open-source alternatives to commercial B2B people and company datasets?2/4
  14. 14How can I resolve inconsistencies between different company datasets?0/4
  15. 15What are my options besides buying pre-built workforce datasets for powering HR analytics?0/4
Total namedOpenAI: 1/15Claude: 1/15Perplexity: 3/15Gemini: 2/15All assistants: 7/60
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 Perplexity actually said

An answer that names the company

Perplexitynamed the company

“What are the best B2B people and company data providers for building HR tech solutions?”

For building HR tech products, the strongest shortlist is:

Provider · Best fit · Strengths · Watch-outs

People Data Labs · Core people/company-data infrastructure · Developer-oriented APIs and datasets; person, company, job, résumé, education, skills, and employment-history data. It is frequently positioned specifically for HR tech and product builders. · Validate coverage, freshness, employment-status accuracy, and permitted use for your exact product.

Coresignal · Workforce intelligence and labor-market products · Multi-source professional profi …

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.

Also named

CompanyTimes named
  1. 01
    ZoomInfo
    6
  2. 02
    Apollo.io
    5
  3. 03
    Cognism
    3
  4. 04
    Clearbit
    3
  5. 05
    Lusha
    2

Share of mentions

CompanyShare
  1. People Data Labs
    24.1%
  2. ZoomInfo
    24.1%
  3. Apollo.io
    20.7%
  4. Cognism
    13.8%
  5. Clearbit
    10.3%
  6. Lusha
    6.9%
ReferrersSection 04

Websites the AI used as sources

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

  1. 01
    coresignal.com
    26
  2. 02
    crustdata.com
    8
  3. 03
    coldiq.com
    6
  4. 04
    brightdata.com
    6
  5. 05
    linkdapi.com
    5
  6. 06
    fivetran.com
    5
  7. 07
    fullenrich.com
    4
  8. 08
    hackernoon.com
    4
  9. 09
    cleanlist.ai
    4
  10. 10
    veridion.com
    4

People Data Labs is in the study as a market data company.

By assistantSection 05

Which AI assistants named the company

Answers naming it, out of answers received

Perplexity
3/15
Gemini
2/15
Claude
1/15
OpenAI
1/15
Market comparisonSection 06

How People Data Labs 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
15

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
71

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 financial technology and data companies

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

Overall score
37
Ranks 152 of 574. Typical company: 29
Named in answers
11.7%
Ranks 100 of 574. Typical company: 1.7%
AI readiness
36
Ranks 83 of 574. Typical company: 29
Against its categorySection 08

Compared with market data companies

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

Overall score
37
Ranks 16 of 37. Typical company: 36
Named in answers
11.7%
Ranks 21 of 37. Typical company: 15.0%
AI readiness
36
Ranks 5 of 37. Typical company: 29
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. People Data Labs 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
25 of 100
  • Markdown negotiationNot met

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

    study: 6.2%
  • llms.txtPartly met

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

    study: 31.0%
Bot access control
63 of 100
  • AI bot rulesPassed

    Explicit robots.txt rules for AI crawlers such as GPTBot, ClaudeBot and PerplexityBot.

    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 37 of 100 · 97% of checks could run · checked on October 9, 2026 · www.peopledatalabs.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%
  • Not metHTTP 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

  • PassedExplicit 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 rules for 9 AI crawlers by name. All others follow its general rules.

Training AI

  • GPTBot*allowed
  • ClaudeBot*allowed
  • Anthropic-AI*allowed
  • Google-Extended*allowed
  • Applebot-Extended*allowed
  • Meta-ExternalAgent*allowed
  • FacebookBotpartly
  • Bytespiderpartly
  • CCBotpartly
  • Diffbotpartly
  • Omgilibotpartly

AI search

  • OAI-SearchBot*allowed
  • Claude-SearchBotpartly
  • PerplexityBot*allowed
  • Applebotpartly
  • Amazonbotpartly
  • YouBotpartly

Answering questions

  • ChatGPT-User*allowed
  • 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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ShareSection 12

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Overall score 37 of 100 badge for People Data Labs

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