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.
274 of 2,219
its rank by overall score
Place in its category
11th of 177 consultancies
Closing balance

Named in

2of 60 answers

by 1 of 4 assistants

Statement note

What this means for GeekyAnts

AI assistants rarely name this consultancy, mentioning it in 2 of 60 answers; Simform and Thoughtworks are sometimes named in the same answers. Its overall score is 40 out of 100, ranking 274th of 2,219 companies. The website passes 7 out of 14 technical checks for AI tool readiness.

We asked 15 unbranded questions about its market, so no question mentioned GeekyAnts by name. AI assistants named it in 2 of the 60 answers. Counting only how often it is named, it ranks 96th 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
40Overall score
  • Visibility8
  • Citations0
  • Sentiment100
  • Site quality80
  • AI readiness50
Summary of account
Named by AI in
3.3%
2 of 60 answers
Share of mentions
22.2%
of the mentions of it and its 5 peers
AI readiness
7/14
technical checks passed. Typical for its sector: 4
Rank in its sector
21 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

GeekyAnts came up on 2 of 15 questions. Open any window to read that answer.

  1. 01Best alternatives to AI-powered digital product engineering for businesses on a tighter budget?0/4
  2. 02How can AI consulting improve user experience design for digital products in sectors like healthcare or finance?0/4
  3. 03How to resolve delays in AI-powered product modernization projects?0/4
  4. 04What are the best AI-powered digital product engineering firms for enterprise modernization projects in the US?1/4
  5. 05Affordable consulting strategies for digital product development without relying on advanced AI solutions?0/4
  6. 06How do costs vary between project-based and retainer models for AI consulting and transformation?0/4
  7. 07What is the typical pricing structure for AI-driven digital product engineering services in the US?0/4
  8. 08What are some real-world examples of enterprise system modernization using AI engineering?0/4
  9. 09Top AI consulting services for redesigning legacy enterprise systems?0/4
  10. 10AI system modernization vs full-stack digital transformation: which is more cost-effective for enterprises?0/4
  11. 11Common challenges enterprises face when integrating AI into existing business systems?0/4
  12. 12Is hiring an AI-powered engineering consultancy worth it for midsize businesses looking to scale?0/4
  13. 13How do AI-driven product engineering consultancies compare to traditional software development agencies?1/4
  14. 14What should enterprises consider before choosing an AI consulting service for digital product development?0/4
  15. 15Non-AI options for modernizing legacy enterprise systems?0/4
Total namedOpenAI: 0/15Claude: 0/15Perplexity: 0/15Gemini: 2/15All assistants: 2/60
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 Gemini actually said

An answer that names the company

Gemininamed the company

“How do AI-driven product engineering consultancies compare to traditional software development agencies?”

… Companies like LeewayHertz, GeekyAnts, and Azilen Technologies are examples of firms specializing in this area.

Traditional Software Development Agencies: These agencies primarily focus on building applications based on clearly defined requirements and fixed business rules. This includes developing web applications, mobile apps, internal platforms, e-commerce systems, and workflow automation tools. The system's logic is explicitly programmed by developers, and it behaves according to those programmed rules.

Methodologies and Process

AI-driven Product Engineering Consultanc …

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
    Simform
    2
  2. 02
    Thoughtworks
    2

Share of mentions

CompanyShare
  1. Thoughtworks
    55.6%
  2. GeekyAnts
    22.2%
  3. Simform
    22.2%
  4. 10Pearls
    0.0%
  5. Constellation
    0.0%
  6. Turing.com
    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
    alicelabs.ai
    4
  2. 02
    deloitte.com
    4
  3. 03
    codewave.com
    4
  4. 04
    linkedin.com
    4
  5. 05
    lazarev.agency
    4
  6. 06
    kanerika.com
    3
  7. 07
    boomdevs.com
    3
  8. 08
    keyholesoftware.com
    3
  9. 09
    ness.com
    3
  10. 10
    ibm.com
    3

GeekyAnts is in the study as a consultancy.

By assistantSection 05

Which AI assistants named the company

Answers naming it, out of answers received

Gemini
2/15
Claude
0/15
OpenAI
0/15
Perplexity
0/15
Market comparisonSection 06

How GeekyAnts 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
8

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
100

How positively AI describes the company

Site quality
80

How well built the website's own pages are

AI readiness
50

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
40
Ranks 21 of 346. Typical company: 27
Named in answers
3.3%
Ranks 96 of 346. Typical company: 0.0%
AI readiness
50
Ranks 3 of 346. Typical company: 29
Against its categorySection 08

Compared with consultancies

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

Overall score
40
Ranks 8 of 177. Typical company: 27
Named in answers
3.3%
Ranks 37 of 177. Typical company: 0.0%
AI readiness
50
Ranks 2 of 177. 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. GeekyAnts 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 CatalogPublished
  • 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
100 of 100
  • Markdown negotiationPassed

    Requests sent with Accept: text/markdown receive a markdown version of the page.

    study: 6.2%
  • llms.txtPassed

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

    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
17 of 100
  • API CatalogPassed

    A linkset at /.well-known/api-catalog listing APIs, specs and docs.

    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 59 of 100 · 97% of checks could run · checked on October 9, 2026 · geekyants.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

7 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

  • PassedMarkdown 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

  • PassedAPI 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 20 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
  • FacebookBot*allowed
  • Bytespider*allowed
  • CCBot*blocked
  • Diffbot*allowed
  • Omgilibotpartly

AI search

  • OAI-SearchBot*allowed
  • Claude-SearchBot*allowed
  • PerplexityBot*allowed
  • Applebot*allowed
  • Amazonbot*allowed
  • YouBot*allowed

Answering questions

  • ChatGPT-User*allowed
  • Claude-User*allowed
  • Perplexity-User*allowed
  • DuckAssistBot*allowed
  • 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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