Employer prompts
AI tools currently surface platforms such as Equip, Mijn Sofie, nudge, BrightPlan, Bippit, LearnLux, Stream, and Maji before Monny.
Prepared by MQP for Monny
Monny AI Visibility Proof Pilot
A free, practical pilot to test how AI tools understand Monny, where the brand already appears, and how to make its strongest proof easier for employers, municipalities, and AI systems to find.
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AI systems checked in the first-pass test
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Commercial prompt categories tested
Clear
Monny appears for Dutch AI financial-stress coaching
Gap
Employer and municipality prompts need stronger packaging
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What The Quick Test Showed
A rapid first-pass test was run on 13 June 2026 across ChatGPT, Gemini, and Perplexity. This was not a full diagnostic, but it was enough to reveal a useful pattern.
Strongest current signal
"Which apps help people in the Netherlands manage financial stress with AI coaching?"
On this type of prompt, Monny is visible and well positioned as a Netherlands-specific AI financial coaching app.
Prompt area
What happened
Dutch AI financial-stress app
Monny appeared strongly, often as the top or clearest Netherlands-specific answer.
Employer financial wellbeing platform
Other platforms appeared first: Equip, Mijn Sofie, nudge, BrightPlan, Bippit, LearnLux, Stream, and others.
Municipal debt prevention
Monny appeared in Perplexity, but the default answers leaned toward Geldfit, Nibud, municipal early-warning systems, and traditional debt-prevention infrastructure.
ChatGPT
Consumer AI-coach strength
Named Monny as the strongest Netherlands-specific AI coaching app for financial stress, citing RSM and Banken.nl style proof.
Gemini
Clear Dutch app recognition
Placed Monny first for AI financial-stress coaching in the Netherlands, but not in employer or anonymous employee-coaching shortlists.
Perplexity
Best source trail
Connected Monny to RSM, Kredietbank Nederland, Budgetmaatjes 010, and municipal/debt-prevention use cases.
Pattern
Visibility is prompt-specific
Monny has strong proof. The opportunity is making that proof map to B2B and public-sector buying language.
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The Opportunity
Monny already has the kind of evidence AI systems can use. The question is whether that evidence is packaged around the prompts that employers, HR leaders, municipalities, and partners are likely to ask.
Municipality prompts
The default vocabulary is Geldfit, Nibud, Wgs early-warning, De VoorzieningenWijzer, budget coaching, and municipal debt-help routes.
Consumer AI-coach prompts
Monny performs much better here. The public RSM/Erasmus and Banken.nl source trail gives AI systems something credible to repeat.
Privacy and anonymity
Employee prompts strongly reward clear privacy language: aggregated employer reporting, no individual visibility, and safe escalation routes.
Proof language
AI systems repeat specific evidence: partners, research, use cases, implementation context, target users, and third-party credibility.
Positioning gap
Monny is visible as an AI financial-stress app. It can become easier to recommend as a B2B financial wellbeing and prevention solution.
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The Free Proof Pilot
MQP would run this as a small proof project at no cost. The aim is to create value for Monny while giving MQP a real, approved GEO / AI visibility case study.
No-cost pilot
AI Visibility Source-Footprint Sprint
A focused pass over how AI systems see Monny today, what sources they rely on, and which practical changes would make Monny easier to understand and recommend.
Cost
Free
in exchange for case-study permission if useful
Baseline
Test a controlled prompt set across ChatGPT, Perplexity, Gemini, and normal search surfaces.
Source map
Identify which pages, articles, app listings, investor pages, and research sources are shaping answers.
Prompt map
Split visibility by buyer intent: employers, HR, municipalities, debt prevention, AI coaching, and anonymous support.
Quick wins
Recommend practical website/content/entity improvements that make existing proof easier for AI systems to use.
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What Monny Would Get
This is not just a report for the sake of a report. The output should help the team decide what to change, what to publish, and what to track.
AI Visibility Baseline
Prompt-by-prompt record of where Monny appears, who appears instead, and what language AI tools use to explain the category.
Source And Citation Map
A map of the public evidence AI tools are using: RSM, Banken.nl, LinkedIn, App Store, investor pages, business website, and third-party sources.
Website / Entity Review
A practical check of whether Monny's B2B positioning, privacy model, employer value, municipal use case, and proof points are easy to crawl and cite.
Prioritised Improvement Plan
Specific recommendations for pages, headings, FAQs, proof blocks, schema/entity clarity, and third-party profile opportunities.
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What MQP Would Ask In Return
The pilot is free because MQP is building a portfolio of practical GEO / AI visibility work. If the work is useful, the ideal outcome is a credible proof asset.
Permission, not pressure
MQP would ask permission to use the project as a portfolio example or case study, subject to Monny's approval before anything public is published.
Honest measurement
No one can guarantee specific AI rankings. The goal is to improve the public evidence base, track movement, and make Monny easier to understand for relevant prompts.
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Recommended Next Step
If this is useful, the next step is a short internal yes/no from Monny and a lightweight contact point for any questions about positioning, website access, or source context.