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See which money questions your brand is trusted to answer.

Measure how AI shortlists your accounts, lending programmes and member services, then trace each answer back to the product facts and public sources that support it.

See every shortlist you’re on.

Buyers ask the same thing a hundred ways, and each question gets its own picks. Track the questions that matter and see how far you are from the top.

New business accounts

Avenholt Bank 1st, in 36 of 40

Mobile banking

Avenholt Bank 1st, in 37 of 40

Emergency savings

Avenholt Bank 4th, in 14 of 40

Savings access

Avenholt Bank 2nd, in 30 of 40

First-home lending

Avenholt Bank 4th, in 21 of 40

  • Avenholt Bank
  • Rivals
  • Your gap to first

40 AI answers per question. Height shows how many named each brand.

Explore the evidence

Give AI a reason to pick you.

Trakkr reads the pages behind every pick. When AI can’t find what it needs, Trakkr writes the fix and ships it once you approve.

  • You approve every change
  • Ships to Shopify, WordPress, Webflow and GitHub
Dorswell Savings

Easy-access savings

38of 40

Why AI picks it

  • States withdrawal rulesDorswell Savings example page
  • Names deposit protectionDorswell Savings example guide
ChatGPT says

“States when savings can be withdrawn.”

Fix ready1 of 3
avenholt-bank.test

State savings access and cover. Verify the details with the team before publishing.

Avenholt Bank

Everyday savings

14of 40
  • Withdrawal rules are missing.Avenholt Bank example page
“Safest place for an emergency fund with strong mobile banking”40 answers

Follow every pick to your site.

Connect Google Analytics to see AI referral visits and landing pages. Recorded conversions depend on the events and values configured in your property.

30 days · AI referral visits

Everyday savings

/services
214 visits
214 AI visitsExample fix

Services and fit

/solutions
96 visits
96 AI visits

About the team

/about
141 visits
141 AI visits

Contact

/contact
118 visits
118 AI visits

AI referral visits

30-day total
569 visitsFour landing pages · Illustrative AI referrals

Mini bars show 30 days of AI visits.

Google Analytics attribution

Illustrative banks and credit unions example. All providers, products, answers, counts and results in this demo are fictional. Verify facts before using an example draft. Industry-specific evidence and measurement caveats follow below.

AI search for financial institutions, explained.

AI search for banks and credit unions means measuring whether ChatGPT, Google AI, Perplexity and Gemini mention, compare and cite a financial institution when people ask about checking, savings, business banking, mobile service or home lending. Trakkr keeps the prompt, audience, product, region, answer and source trail together, so marketing, product and compliance teams can see the gap and route the right fix.

No financial advice. Demo figures are illustrative.

Tracked across
  • ChatGPT
  • Google
  • Perplexity
  • Gemini
AI shortlist watch
Illustrative

Emergency savings · National · ChatGPT

Safest place for an emergency fund with strong mobile banking

Tracked institution

Avenholt Bank

Evidence is incomplete for this buying moment.

#3of 6

Trust

68%

Citations

11/16

Route

Accuracy

Next action

Route to product-page accuracy and an approved emergency-fund explainer.

The shortlist now starts before the product page.

A customer states the need once. The answer combines product facts, public evidence and local context before the institution gets a visit.

  1. 01

    Ask

    One money question replaces ten tabs.

    A buyer states the goal, product, location and constraint in one prompt.

  2. 02

    Shortlist

    The answer narrows the market.

    AI names a small set of institutions before the buyer reaches a product page.

  3. 03

    Trust

    Public evidence decides what survives.

    Official disclosures, product facts, regulators, publishers, reviews and local evidence shape the explanation.

  4. 04

    Act

    Each gap gets a named route.

    Teams fix the page, markup, explainer, review evidence, regional coverage or approved third-party source.

Open the full readout.

Switch views to see how Trakkr breaks down visibility for banks and credit unions, from the market to the work that moves it.

Avenholt BankExample readout

See the product, audience and region behind the average.

A national visibility score cannot tell a retail banking team where a specific buying moment breaks. Keep each dimension attached.

Avenholt Financial Group

Four fictional institutions across eight illustrative markets

Every institution, score, prompt result and trend below is fixed illustrative product data. Public brands appear only in an unscored peer set and are not Trakkr customers.

AI visibility
62.8
Across the illustrative product and audience set
Tracked prompts
1,248
Fixed demo variants across four AI engines
Regulated-source share
21.7%
Illustrative citations from government and regulator sources
Open evidence gaps
137
Mapped to an owner and remediation route

Product and audience visibility

The average hides where a specific product stops being trusted for a specific audience.

Visibility index · illustrative

Everyday checking

Clarify mobile deposit and ATM access for new businesses.

New LLC
58
Emergency saver
43
Mobile-first member
76
First-time buyer
49

Savings

Connect emergency-fund education to the current product facts.

New LLC
39
Emergency saver
71
Mobile-first member
67
First-time buyer
53

Business banking

Build stronger independent evidence for LLC formation questions.

New LLC
67
Emergency saver
31
Mobile-first member
45
First-time buyer
38

Home lending

Add regional programme and eligibility context without making advice claims.

New LLC
35
Emergency saver
42
Mobile-first member
47
First-time buyer
64
Regional trust mapVisibilityTrust

Raleigh-Durham, NC

First-time buyer education

Visibility74
Trust81%

County programme pages are not consistently cited.

Denver, CO

Emergency savings

Visibility66
Trust72%

Comparison publishers cite an older education page.

Phoenix, AZ

Mobile-first checking

Visibility57
Trust61%

App-review evidence is mixed around mobile deposit.

Atlanta, GA

New LLC banking

Visibility49
Trust54%

Regional business pages do not state service coverage clearly.

View 1 of 5

Measure what the buyer asked, what the model said and why.

The prompt, audience, product, market, engine, answer, citations and check date stay attached. That is what makes a result useful and reviewable.

01

How does AI discovery change banking acquisition?

The buyer can ask one detailed money question and receive a short list before opening a bank or credit union website. That makes the answer itself an acquisition surface. Product facts, regional relevance and cited public evidence now shape which institutions reach the comparison stage.

02

Which prompts matter?

Start with questions that combine a financial goal, a product, an audience and a location or constraint. Business accounts for a new LLC, emergency savings, mobile-first credit union access and first-time home-buyer education all create clearer diagnostic work than a broad brand prompt.

03

Which sources influence answers?

AI systems can draw from official product and disclosure pages, government and regulator guidance, finance publishers, app stores, consumer reviews, local profiles, education sites and communities. The useful question is not only whether a source cites the brand, but which claim and buying moment it supports.

04

What should teams do next?

Tie each lost or qualified answer to a specific evidence gap. Route that gap to product-page accuracy, structured data, educational content, review evidence, regional pages or approved third-party source work, with compliance review where needed.

How the measurement works

  1. 01Model the buying moments

    Build prompt groups by audience, product, need, region and constraint. Keep financial-advice prompts separate from product-discovery prompts.

  2. 02Capture the complete answer

    Store the engine, model context, prompt, answer, shortlist position, named peers, claim language, citations and check date.

  3. 03Test entity and claim alignment

    Compare owned pages, disclosures, regulator records, publishers, reviews and regional surfaces without treating any one source as the whole truth.

  4. 04Measure at the useful grain

    Report visibility by product, audience and market so a national average cannot hide a local or programme-level gap.

  5. 05Route work with evidence

    Give each finding an owner, source trail, approval path and expected answer signal. Recheck after the underlying source changes.

Every institution, score, prompt result and trend below is fixed illustrative product data. Public brands appear only in an unscored peer set and are not Trakkr customers.

Questions, answered.

What financial services teams ask before they start.

No. Trakkr measures how AI systems describe, shortlist and cite institutions. It does not recommend an account, verify a consumer's eligibility or decide whether a financial product is suitable.

Ask anything elseAsk anything else

The Agent answers from our docs, methods and pricing, and links to the source.

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Every question your buyers askchecked daily in eight AI models