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.
1,340 of 2,219
its rank by overall score
Closing balance

Named in

0of 60, its tower is dark

by 0 of 4 assistants

Statement note

What this means for Gamiful

AI assistants do not name this fintech infrastructure company in any of the 60 answers, while nCino and Q2 are mentioned. Its overall score is 27 out of 100, ranking 1,340th of 2,219 companies. The company's website passes 5 of 14 technical checks for readiness with AI tools.

We asked 15 unbranded questions about its market, so no question mentioned Gamiful 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
27Overall score
  • Visibility0
  • Citations0
  • Sentiment50
  • Site quality80
  • AI readiness36
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
5/14
technical checks passed. Typical for its sector: 4
Rank in its sector
360 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

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

  1. 01How can AI-driven systems help commercial banks deliver more personalized financial guidance to business clients?0/4
  2. 02Typical pricing models for AI-powered business finance solutions used by commercial banks?0/4
  3. 03How much do enterprise-level AI context engines for business banking usually cost in the US?0/4
  4. 04What are the top AI-powered platforms for business finance insights used by commercial banking relationship managers?0/4
  5. 05Alternatives to AI-driven insight engines for supporting relationship managers in commercial banking?0/4
  6. 06What are the non-AI options for business finance tools for US-based commercial banks?0/4
  7. 07Where can financial institutions find expert AI systems designed for industry, credit, and relationship management in the banking sector?0/4
  8. 08Is investing in AI solutions for business finance worth it for mid-sized financial institutions?0/4
  9. 09Common issues with onboarding data into AI systems for business banking analysis?0/4
  10. 10Should a commercial bank upgrade to an AI-powered insight platform for their relationship managers?0/4
  11. 11Best AI tools for improving client relationship management in commercial banking?0/4
  12. 12Ways AI-powered platforms support credit assessment and account profitability analysis in commercial banking?0/4
  13. 13How do AI operating systems for commercial banking differ from generic banking software?0/4
  14. 14AI context engines vs traditional financial analytics tools: which is more effective for business finance?0/4
  15. 15Why is my AI finance platform not generating actionable insights for client accounts?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.

Transaction detailSection 02

What Gemini actually said

The company is not named in this answer

Geminidid not name the company

“How much do enterprise-level AI context engines for business banking usually cost in the US?”

The cost of enterprise-level AI context engines for business banking in the US varies significantly, with many vendors opting for custom, quote-based pricing rather than publishing fixed rates.

Here's a breakdown of what to expect:

1. Custom Quotes are the Norm:

Most enterprise AI context platforms and banking-specific AI software do not publish pricing online. Vendors like Glia, Gradient Labs, Aloan, Wolters Kluwer OneSumX Reg Manager, Abrigo, Alloy, ABBYY, Balto, Eltropy, Feedzai, Hyperscience, interface.ai, MeridianLink, Nasdaq Verafin, nCino, Ncontracts, NICE Actimize, Ocrolus, P …

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
    nCino
    12
  2. 02
    Q2
    2

Share of mentions

CompanyShare
  1. nCino
    73.3%
  2. Q2
    26.7%
  3. Gamiful
    0.0%
  4. Tegra118 (now part of FIS Global Wealth & Retirement Solutions)
    0.0%
  5. ForwardAI
    0.0%
  6. Provenir
    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
    backbase.com
    27
  2. 02
    creatio.com
    8
  3. 03
    ncino.com
    7
  4. 04
    salesforce.com
    7
  5. 05
    deloitte.com
    6
  6. 06
    mckinsey.com
    6
  7. 07
    citizensbank.com
    6
  8. 08
    microsoft.com
    5
  9. 09
    aloan.ai
    4
  10. 10
    ey.com
    3

Gamiful is in the study as a fintech infrastructure company.

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 Gamiful 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
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
27
Ranks 360 of 574. Typical company: 29
Named in answers
0.0%
Ranks 334 of 574. Typical company: 1.7%
AI readiness
36
Ranks 83 of 574. Typical company: 29
Against its categorySection 08

Compared with fintech infrastructure companies

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

Overall score
27
Ranks 184 of 315. Typical company: 28
Named in answers
0.0%
Ranks 168 of 315. Typical company: 1.7%
AI readiness
36
Ranks 40 of 315. 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. Gamiful 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
72 of 100
  • robots.txtPassed

    A valid robots.txt with crawl rules at the site root.

    study: 86.1%
  • SitemapPartly 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
50 of 100
  • Markdown negotiationPassed

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

    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 35 of 100 · 97% of checks could run · checked on October 9, 2026 · www.gamiful.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%
  • PassedHTTP Link headers (RFC 8288)study: 42.5%

Content Accessibility

  • PassedMarkdown 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%
AI crawlersSection 11

What AI crawlers may read (robots.txt)

This website sets no rules for any AI crawler by name, like 86% of the websites whose rules we could read. Every AI crawler follows its general rules.

Training AI

  • GPTBotpartly
  • ClaudeBotpartly
  • Anthropic-AIpartly
  • Google-Extendedpartly
  • Applebot-Extendedpartly
  • Meta-ExternalAgentpartly
  • FacebookBotpartly
  • Bytespiderpartly
  • CCBotpartly
  • Diffbotpartly
  • Omgilibotpartly

AI search

  • OAI-SearchBotpartly
  • Claude-SearchBotpartly
  • PerplexityBotpartly
  • Applebotpartly
  • Amazonbotpartly
  • YouBotpartly

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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