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 to 14, 2026
when the AI assistants were asked
Account no.
1,557 of 2,219
its rank by overall score
Place in its category
42nd of 64 agencies
Closing balance

Named in

0of 60, its tower is dark

by 0 of 4 assistants

Statement note

What this means for Eleris

AI assistants do not name this agency in any of their 60 answers. Its overall score is 26 out of 100, which ranks 1,557th of 2,219 companies, similar to the typical company in its sector. The website passes 4 of 14 technical checks for readiness with AI tools.

We asked 15 unbranded questions about its market, so no question mentioned Eleris by name. AI assistants named it in none of the 60 answers. Counting only how often it is named, it ranks 149th of 346 in its sector. The typical advisory or services firm is named in 0.0% 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
26Overall score
  • Visibility0
  • Citations0
  • Sentiment50
  • Site quality80
  • AI readiness29
Summary of account
Named by AI in
0.0%
0 of 60 answers
Share of mentions
0.0%
of the mentions of it and its 5 peers
AI readiness
4/14
technical checks passed. Typical for its sector: 4
Rank in its sector
204 of 346
by overall score, where the typical advisory or services firm scores 27

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Line itemsSection 01

Every question, and where the company came up

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

  1. 01What salary ranges can marketers expect at US-based consumer product portfolio companies?0/4
  2. 02Multi-brand portfolios vs. incubators: what's the difference for marketers?0/4
  3. 03Is joining a fast-paced consumer product portfolio company worth it for career growth?0/4
  4. 04Problems that can arise when scaling multiple in-house brands simultaneously0/4
  5. 05Other career paths besides working at in-house consumer product portfolios0/4
  6. 06How do multi-brand consumer product portfolios support rapid experimentation in marketing?0/4
  7. 07What are the top multi-brand consumer product portfolios for high-growth brands?0/4
  8. 08Ways high-growth consumer product teams turn shopper attention into repeat customers0/4
  9. 09Best US companies known for building and scaling multiple consumer brands in-house0/4
  10. 10Best strategies for entrepreneurs interested in launching data-driven consumer products0/4
  11. 11Should entrepreneurs consider working with companies specializing in rapid product testing and scaling?0/4
  12. 12How is compensation typically structured for employees in fast-growing consumer brand groups?0/4
  13. 13How do multi-brand consumer product operators compare to traditional single-brand companies?0/4
  14. 14Common challenges faced by marketers working at multi-brand consumer product groups0/4
  15. 15Alternatives to joining a multi-brand consumer product operator for entrepreneurs0/4
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.

Transaction detailSection 02

What OpenAI actually said

The company is not named in this answer

OpenAIdid not name the company

“What are the top multi-brand consumer product portfolios for high-growth brands?”

Below are leading examples of multi‑brand consumer product portfolios that consistently create or acquire high‑growth brands — grouped by sector with a short rationale for why each portfolio is effective. I cite representative sources for each item so you can verify details.

Enterprise global consumer goods (broad scale, multi‑tier portfolios)

L’Oréal — luxe-to‑mass beauty portfolio (Lancôme, Kiehl’s, La Roche‑Posay, L’Oréal Paris, Maybelline, NYX). Strengths: multi‑tier positioning (mass → prestige → dermocosmetics), heavy R&D, global distribution and fast trend response. (loreal.com …

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.

Named instead

CompanyTimes named
  1. 01
    Thrasio
    1

Share of mentions

CompanyShare
  1. Thrasio
    100.0%
  2. Eleris
    0.0%
  3. Pattern Brands
    0.0%
  4. Foundry Brands
    0.0%
  5. Win Brands Group
    0.0%
  6. Berlin Brands Group (US)
    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
    linkedin.com
    7
  2. 02
    entrepreneur.com
    6
  3. 03
    shopify.com
    6
  4. 04
    smashbrand.com
    4
  5. 05
    ziprecruiter.com
    4
  6. 06
    mckinsey.com
    4
  7. 07
    bain.com
    3
  8. 08
    vivaldigroup.com
    3
  9. 09
    thebrandconsultancy.com
    3
  10. 10
    glassdoor.com
    3

Eleris is in the study as an agency.

By assistantSection 05

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 06

How Eleris 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
80

How well built the website's own pages are

AI readiness
29

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 advisory and services firms

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

Overall score
26
Ranks 204 of 346. Typical company: 27
Named in answers
0.0%
Ranks 149 of 346. Typical company: 0.0%
AI readiness
29
Ranks 96 of 346. Typical company: 29
Against its categorySection 08

Compared with agencies

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

Overall score
26
Ranks 36 of 64. Typical company: 27
Named in answers
0.0%
Ranks 24 of 64. Typical company: 0.0%
AI readiness
29
Ranks 24 of 64. 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?

We could not tell. The website turned away our requests for the files an agent would read. An agent arriving by itself is turned away the same way.

Agent payment protocols
  • x402Not present
  • MPPCould not check
  • AP2Not present
  • ACPCould not check
  • UCPCould not check
Interfaces for AI agents
  • MCP Server CardCould not check
  • A2A Agent CardCould not check
  • Agent SkillsCould not check
  • API CatalogCould not check
  • llms.txtCould not check
  • 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
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.txtCould not check

    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 AuthCould not check

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

    study: 0.3%
APIs, auth and MCP
  • API CatalogCould not check

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

    study: 1.3%
  • OAuth discoveryCould not check

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

    study: 6.3%
  • OAuth Protected ResourceCould not check

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

    study: 5.3%
  • auth.mdCould not check

    Publish an auth.md describing how agents sign in.

    study: 0.2%
  • MCP Server CardCould not check

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

    study: 0.8%
  • A2A Agent CardCould not check

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

    study: 0.2%
  • Agent SkillsCould not check

    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 manifestCould not check

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

    study: 0.2%

Score 45 of 100 · 53% of checks could run · checked on October 9, 2026 · www.eleris.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

4 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%
  • Not metllms.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 10 AI crawlers by name. All others follow its general rules.

Training AI

  • GPTBotpartly
  • ClaudeBotpartly
  • Anthropic-AIpartly
  • Google-Extended*blocked
  • Applebot-Extended*blocked
  • Meta-ExternalAgent*blocked
  • FacebookBot*blocked
  • Bytespider*blocked
  • CCBot*blocked
  • Diffbot*blocked
  • Omgilibot*blocked

AI search

  • OAI-SearchBotpartly
  • Claude-SearchBotpartly
  • PerplexityBotpartly
  • Applebotpartly
  • Amazonbot*blocked
  • YouBot*blocked

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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