A dating app positioned against swipe-fatigue: instead of left/right on photos, users answer prompts about values and the ML algorithm surfaces compatible matches with explainability ('you both prioritize career flexibility, both prefer city living'). Mobile-first (iOS + Android), with E2EE messaging, photo liveness verification, and premium subscription via App Store IAP. Targeting professionals 25-40 in the US/EU. Trust & safety is the central differentiator.
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Full graph Stakeholder Structural Requirements Context Everything — stakeholders, requirements, elements, and all their connections.
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Stakeholder Requirement Element Connection
Stakeholders 21 Founder / CEO critical vision + investor relations Product Manager feature prioritization + roadmap ML Engineer matching algorithm + image moderation models Trust & Safety Lead critical moderation + user safety + abuse response iOS Developer native iOS engineering Android Developer native Android engineering Backend Engineer API + services + infrastructure Product Designer UI + UX + brand Growth Marketing Lead paid acquisition + ASO + content User: professional, 25-35 critical primary target demographic User: 50+ secondary demographic, growing User: LGBTQ+ secondary demographic, vocal Apple App Review critical App Store gatekeeper Google Play Review Play Store gatekeeper Stripe (alt-payment provider) non-IAP payment processor Twilio SMS verification provider Persona photo liveness + ID verification API Lead GDPR Supervisory Authority (Irish DPC) critical EU data protection enforcement US FTC / KOSA enforcement US child safety + privacy regulator Series A investor (lead VC) growth + governance Competitive landscape (Hinge, Bumble, etc.) market context Needs 20 I want matches who actually share my values, not just look attractive P1 User: professional, 25-35 I want to feel safe — confidence that profiles are real and bad actors get banned P1 User: professional, 25-35 I want to understand why a match was suggested, not be matched by a black-box algorithm P2 User: professional, 25-35 I want my data not sold to advertisers or data brokers P2 User: professional, 25-35 I want to pay only for premium features I actually want, not be bundled P3 User: professional, 25-35 I want a simple interface — large text, no swipe gestures I have to learn P1 User: 50+ I don't want push notifications late at night P2 User: 50+ I want my actual gender identity respected (not forced to pick from male/female) P1 User: LGBTQ+ I want matching to respect my sexual orientation precisely (not assume hetero default) P1 User: LGBTQ+ I need to ban a confirmed bad actor across all platforms within an hour P1 Trust & Safety Lead I need an automated pre-filter for image moderation so my human reviewers only see edge cases P1 Trust & Safety Lead I need explicit, granular, capture-able consent per data-processing category P1 Lead GDPR Supervisory Authority (Irish DPC) I need data export and deletion within 30 days of user request P1 Lead GDPR Supervisory Authority (Irish DPC) I need transparency about the matching algorithm under the EU DSA P2 Lead GDPR Supervisory Authority (Irish DPC) I require all in-app digital purchases to go through IAP (App Store Guideline 3.1.1) P1 Apple App Review I require data collection categories to be accurately disclosed in App Store privacy nutrition label P1 Apple App Review I require effective age verification to keep under-18 users off the platform P1 US FTC / KOSA enforcement I need to reach 100k Monthly Active Users within 12 months to unlock the Series B P1 Founder / CEO I need to see 15% MoM MAU growth sustained over 6 months P1 Series A investor (lead VC) I need to train models without exposing user PII (no plaintext messages or DOBs in training corpus) P1 ML Engineer