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.

SWIFT47/60TRANSUNION46/60EXPERIAN42/60COINSTAR41/60NAVY FEDERAL CREDIT UNION41/60ZELLE41/60BLOOMBERG L.P.38/60TRADINGVIEW38/60BANCO POPULAR DE PUERTO RICO37/60PAYPAL37/60VENMO37/60CHEXSYSTEMS33/60EQUIFAX33/60MORNINGSTAR32/60S&P GLOBAL31/60ALPACA MARKETS30/60COINBASE30/60PINWHEEL29/60STRIPE29/60XERO29/60ACORNS28/60AFFIRM28/60PAYFAST (PAYGATE)28/60PAYONEER28/60INTERACTIVE BROKERS26/60CHARLES SCHWAB19/60THE HARTFORD9/60MOODY'S21/60CME GROUP19/60ACCENTURE11/60CHUBB10/60MORGAN STANLEY6/60AMERICAN EXPRESS14/60SYNCHRONY FINANCIAL12/60FACTSET20/60NASDAQ19/60ALLSTATE11/60WELLS FARGO18/60PAYCHEX10/60ARES MANAGEMENT3/60
Rezolve Ai
MethodDemo
Back to the study
AI visibility statementFinance AI Visibility Study 2026Issued October 14, 2026
Account holder

Open Rewards

openrewards.com

Banking and fintech infrastructureFinancial technology and data

Money20/20 attendee

Statement period
October 9 to 14, 2026
when the AI assistants were asked
Account no.
8 of 2,219
its rank by overall score
Place in its category
1st of 315 fintech infrastructure companies
Closing balance

Named in

13of 60 answers

by 4 of 4 assistants

Statement note

What this means for Open Rewards

AI assistants name this fintech infrastructure company in 21.7% of answers, much more often than the typical company in its sector, which is named in 1.7%. It has an overall score of 52 out of 100, ranking 8th of 2,219 in the study and 1st among 574 in its sector. Its website passes 5 of 14 technical checks for readiness with AI tools.

We asked 15 unbranded questions about its market, so no question mentioned Open Rewards by name. AI assistants named it in 13 of the 60 answers. Counting only how often it is named, it ranks 37th of 574 in its sector. The typical financial technology and data company is named in 1.7% of answers.

Overall score
52Overall score
  • Visibility39
  • Citations15
  • Sentiment100
  • Site quality80
  • AI readiness36
Summary of account
Named by AI in
21.7%
13 of 60 answers
Share of mentions
86.7%
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
1 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

Open Rewards came up on 8 of 15 questions. Open any window to read that answer.

  1. 01How can city governments use digital rewards platforms to boost neighborhood shopping initiatives?1/4
  2. 02How do point-based rewards platforms compare to cashback programs for supporting small businesses?0/4
  3. 03How do community rewards programs work to strengthen local economies?1/4
  4. 04Which types of local rewards programs have the highest participation rates among US consumers?0/4
  5. 05What are the alternatives to app-based shop local rewards platforms for promoting local shopping?0/4
  6. 06Why am I not receiving points after making a purchase at a participating local business?0/4
  7. 07Is joining a city-based shop local rewards program worth it for small business owners?1/4
  8. 08Digital punch cards vs. app-based city rewards programs—what are the main differences?0/4
  9. 09Do digital rewards platforms actually help communities increase local spending?2/4
  10. 10What are some ways consumers can maximize rewards from shopping at local businesses through city programs?2/4
  11. 11What are the best digital rewards platforms for encouraging people to shop locally in the US?4/4
  12. 12Are there non-digital ways to incentivize residents to shop at local businesses?0/4
  13. 13What does it typically cost for a city or local business to participate in a digital shop local rewards platform?1/4
  14. 14How can local businesses resolve issues with customer rewards not tracking correctly?0/4
  15. 15Are there free or low-cost digital rewards options for community programs in small towns?1/4
Total namedOpenAI: 1/15Claude: 4/15Perplexity: 1/15Gemini: 7/15All assistants: 13/60
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 digital rewards platforms for encouraging people to shop locally in the US?”

The strongest options for encouraging local shopping in the US are Open Rewards, Stamp Me, Loca, and a custom loyalty platform such as Kangaroo, Como, or TapMango. The best choice depends on whether the program is citywide, tourism-focused, or run by a merchant association.

Platform · Best for · Why it stands out · Main limitation

Open Rewards · City governments, downtown districts, chambers of commerce · Lets shoppers earn rewards at participating local businesses and redeem them elsewhere in the community; businesses can join without program fees. …

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
    Yiftee
    1

Share of mentions

CompanyShare
  1. Open Rewards
    86.7%
  2. Yiftee
    13.3%
  3. Colu
    0.0%
  4. Think Local First DC
    0.0%
  5. Prizeout
    0.0%
  6. Downtown Dollars (Downtown Fresno Partnership)
    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
    openrewards.com (its own website)
    32
  2. 02
    chandlerflexrewards.com
    11
  3. 03
    stampme.com
    7
  4. 04
    bonusqr.com
    7
  5. 05
    loop.fans
    5
  6. 06
    yesmagazine.org
    5
  7. 07
    business.nextdoor.com
    4
  8. 08
    publicceo.com
    4
  9. 09
    xoxoday.com
    3
  10. 10
    moneycrashers.com
    3

Open Rewards 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

Gemini
7/15
Claude
4/15
OpenAI
1/15
Perplexity
1/15
Market comparisonSection 06

How Open Rewards 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
39

How often AI assistants name the company, and how that compares with its peers

Citations
15

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
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
52
Ranks 1 of 574. Typical company: 29
Named in answers
21.7%
Ranks 37 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
52
Ranks 1 of 315. Typical company: 28
Named in answers
21.7%
Ranks 12 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. Open Rewards publishes a UCP profile with checkout, but the profile lists no way to pay. An agent would have to click through the website like a person.

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

The website runs on Wix.

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
50 of 100
  • Markdown negotiationNot met

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

    study: 6.2%
  • llms.txtPassed

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

    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 54 of 100 · 63% of checks could run · checked on October 9, 2026 · www.openrewards.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

  • Not metMarkdown content negotiationstudy: 7.4%
  • Passedllms.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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ShareSection 12

Share this result

Overall score 52 of 100 badge for Open Rewards

A badge of this result that you can show on your own website. Copy the code below.

<a href="https://aeo-audit.rezolve.com/us/finance/openrewards.com"><img src="https://aeo-audit.rezolve.com/finance/badges/openrewards.com.svg" alt="Overall score 52 of 100, Finance AI Visibility Study 2026" width="280" height="64"></a>
End of statement
All 2,219 companiesHow we measured this
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