Product Requirements Document (PRD)

tl;dr A dating platform that ensures transparency and equality, using verified authenticity to create matches based on verified income for men and certified attractiveness for women. The platform promotes authentic connections within respective social and economic leagues by integrating API tools for verification. Goals Business goals - Establish market leadership: 5% market share of the online dating industry within 2 years. - Revenue: $10M ARR via subscription + premium tiers. - Operational efficiency: direct API integration to limit manual intervention. User goals - Authentic connections — 90% satisfaction in user surveys. - Balanced matches — meaningful connections within verified social leagues. - Privacy assurance — encrypted security for financial and physical verification data. Non-goals - Manual verification at scale (automation-first). - Expansion to other demographic verification axes (out of scope for v1). - Verifying personal interests or hobbies. User stories - As a male user, I want to verify my income through a reliable API to accurately represent my financial standing. - As a female user, I want my avatar to represent my appearance, making my attractiveness rating genuinely authentic. - As a user, I want to be matched with those who share similar social equities to promote fair interactions. User experience Onboarding - Men: guided API integration for income verification with visual cues for smooth data flow. - Women: step-by-step unedited photo upload + certified-avatar generation. Core walkthrough - Men: post-verification dashboard with profiles within respective attractiveness brackets. - Women: post-certification dashboard with profiles within their verified attractiveness tier. - Matches: notify on mutual interest; secure communication channel. Edge cases / advanced - Celebrate non-romantic connection types (friendship, networking). - Real-time profile-enhancement advice. Narrative John, 32, tech entrepreneur. Tired of fake profiles. Verifies his $200K income and joins peers. Emma, 28, marketing professional. Has her attractiveness certified via unedited photographs. The matches aren't appearances or statuses — they're authentic. Success metrics User-centric - 100,000 users within first six months. - NPS ≥ 85. Business - 20% conversion rate on premium tier. Technical - 99.9% API uptime on all verification processes. Technical considerations - API integrations for financial data sourcing. - Scalable image-processing pipeline for photo verification + certified avatar generation. - Encrypted, tiered data storage with regular compliance audits. - Scalable to hundreds of thousands of users with stable response times. Technical challenges - Payment-system integration and data synchronization. - Managing large image datasets for photo processing. Milestones & sequencing Project estimate: Large (6–9 months). Team: Medium (5–6) — PM, API dev, UX/UI, security expert, data scientist, QA. Phases 1. Discovery & planning (4w) — scope + user interviews. 2. Design & prototyping (6w) — wireframes + prototype. 3. Development sprints (12w) — API integration + core functionality. 4. Testing & QA (4w) — user testing + refinement. 5. Beta release (2w) — invite-only with targeted feedback. 6. Launch prep (4w) — final tweaks + marketing. 7. Go-live — strategic launch + influence campaigns. 8. Post-launch evaluation (4w) — user feedback + tweaks.

tl;dr The platform leverages artificial intelligence to curate personalized video narratives from users' photos, journal entries, and home videos, transforming static memories into dynamic, emotional stories. Problem statement Photos, videos, and personal stories are fragmented across platforms; reliving memories feels disjointed and passive. People want more than an archive — they seek meaningful ways to reflect on and emotionally reconnect with their past. The platform transforms disparate media into coherent, AI-curated video narratives that evoke emotion. Goals Business goals - 100K MAU in year one (family-oriented markets + digital creators). - Subscription monetization with free trials promoting premium features (longer videos, personalized templates). - Establish brand authority in AI-powered memory preservation. User goals - Seamlessly transform personal media into emotionally-driven video narratives. - Tools to relive favorite moments through highly personalized storytelling. - Share videos with family, friends, and social platforms. Non-goals - Not a comprehensive cloud storage solution — focus is storytelling, not storage. - No extensive photo/video editing suite beyond AI-generated outputs. - Not competing with transactional memory platforms (Google Photos, Dropbox). User stories - As a parent, I want to transform photos and videos of my child's first year into a compelling, emotion-driven video to watch and share with family. - As a digital creator, I want to curate a highlight reel of key travel-vlog moments with AI storytelling that evokes stronger emotional connection. - As a nostalgic individual, I want to revisit my journal entries and old vacation photos in an engaging way through curated, AI-generated visuals. - As a grandparent, I want to pass down my life story by compiling old media into cohesive, visual narratives my family can cherish. User experience (step-by-step) 1. Onboarding — connect media sources (Google Photos, Dropbox, camera roll). Ask the user what types of memories matter (childhood, holidays, milestones). 2. Media upload — user selects photos / videos / journals OR imports a folder. AI auto-sorts and categorizes by timestamp, people, and recurring themes. 3. Customization — preview AI-generated narrative; choose templates ("Nostalgic," "Joyful," "Reflective") and themes. AI auto-selects music + transitions based on emotional tone inferred from the media. User can accept or replace suggestions. 4. AI-generated video — final render stitches moments into a coherent, emotionally resonant story. Preview, share, or download. 5. Engagement — built-in sharing sparks new conversations. Users are prompted to create new stories from unused or newly-uploaded media. Narrative In a world where digital footprints scatter memories across platforms, the platform becomes the emotional curator of your past. Upload your photos, videos, and journal entries; the platform's AI weaves your most cherished moments into a heartfelt retelling of your life. Success metrics - User adoption: 100K active users within year one. - Engagement: average session length 15 minutes. - Retention: 30% monthly retention driven by emotional engagement + social sharing. - Viral coefficient: ≥ 1.1 (each user brings in more than one additional user). Technical considerations - AI video generation — integrate image/video recognition + NLP (journal interpretation) + automated editing. - Privacy & storage — AWS or GCP partnership; GDPR / CCPA compliance. - Integrations — Google Photos, Dropbox, iCloud. - Scalability — cloud-based rendering for video generation. Milestones & sequencing 1. Initial MVP (20 weeks) — basic AI video generation from photos / videos + simple sharing. 2. Expanded functionality (10 weeks) — emotion recognition from journal entries / voice-overs + template and theme customization. 3. Beta launch (10 weeks) — targeted release to family + creator markets; iterate on narrative quality + engagement. 4. Public launch (20 weeks) — full release + marketing; monetization (premium templates, longer videos, expanded storage).

tl;dr Develop an AI-enhanced IoT solution for swift outage detection and reporting — refining operational efficiency and boosting customer engagement through precise anomaly analysis and proactive communication. Goals Business goals - Reduce outage downtime by 20% Y1, targeting 40% by Y2. - Boost customer satisfaction scores +15% Y1, +30% by Y2 via improved transparency. - Optimize resource allocation — +25% maintenance efficiency via data-driven dispatch. User goals - Operators: timely anomaly alerts for proactive response. - Operators: predictive maintenance insights to avert outage disasters. - Customers: consistent updates for informed planning. - Technicians: know where and what to fix to maximize repair efficiency. Non-goals - Consumer-facing standalone product suite (out of scope). - Real-time tariff adjustments tied to outage periods. User stories - As an operator, I want timely alerts for anomalies so I can prevent failures proactively. - As an operator, I need an integrated IoT dashboard for efficient outage monitoring. - As a customer, I want precise outage updates and restoration timelines. - As a technician, I need to know where and what to fix to maximize repair efficiency. User experience Operators install sensors across the grid and monitor infrastructure health via a robust dashboard with real-time data visualizations and maps. On anomaly detection, the AI-powered response system reroutes energy instantly. Customers receive outage updates and restoration forecasts through mobile apps and automated notifications. Narrative A sensor alert signals an anomaly in a transformer. The operator opens the dashboard, sees the situation in real time, and the system preemptively reroutes power — averting a blackout. Maintenance teams, guided by predictive insights, attend to the potential failure ahead of time. Customers receive continuous updates throughout. Success metrics User-centric - Mobile app adoption: 60% in Y1. - NPS: +10 points over baseline. Business - Outage downtime reduction: 20% within Y1. - Outage-management cost savings: 15% by year-end. Technical - System uptime: 99.9%. - Anomaly-alert accuracy: 95% post-deployment. Technical considerations - Comprehensive operator dashboard + robust IoT integration + ML models. - High-performance data processing + secure cloud storage at grid scale. - Integrations: CRM systems (customer comms) + grid-management software. - Encryption + compliance for sensitive infrastructure data. - Scalable to expanding sensor counts and geographic areas. Technical challenges & solutions - Diverse IoT hardware → flexible data ingestion pipeline. - Algorithm accuracy → continuous model training and validation with real-world feedback. Milestones & sequencing Project estimate: large (months to quarters). Team: 6+ — IoT specialists, data scientists, UX/UI, software engineers, QA, PMs. Phases 1. Discovery & planning (4w) — system requirements + refined plan. 2. Design & prototyping (5w) — initial designs + data-flow models + prototype dashboard. 3. Development sprints (12w) — integrated IoT + AI systems. 4. Testing & QA (6w) — rigorous real-world testing + QA reports. 5. Beta release (4w) — feedback-driven adjustments. 6. Launch prep (3w) — final polish + docs. 7. Go-live & evaluation (4w) — official launch + post-launch impact analysis.

Decision Replace the 7-step setup wizard with a 2-step setup + in-product checklist for all new trials. Why this Funnel data shows 38% of trials abandon at step 4 (workspace config). Five-cohort regression shows no correlation between completing steps 4–7 and day-30 retention (r = 0.04). Step 4 is config work that can be deferred without breaking value. Impact Trial-to-paid: 12% → 17% over 8 weeks (+5pp). Day-1 activation: 41% → 60%. TTFV: 14 days → 8 days. Scope In scope: 1. New 2-step setup: workspace name + first project. 2. In-product checklist surfacing remaining setup as opt-in. 3. Admin override to skip checklist entirely for sales-assist accounts. 4. Activation event re-defined as "3 of 5 events in first 7 days". Out of scope: - Pricing changes. - Reverse-trial gating. - Email cadence rewrite (separate request). Risks - Sales-assist accounts expect a guided setup. Mitigation: admin override + CSM-triggerable fallback flow. Owner: Marcus (CRO). - Existing dashboards instrument the old funnel. Mitigation: dual-write events for 4 weeks. Decision criteria & kill Ship if instrumented week-over-week activation lift ≥ 4pp by week 3. Roll back if activation drops > 2pp or sales-assist NPS drops > 5 points. Next step Eng scoping locked Friday. Activation instrumentation lands week 1. First cohort enters Monday.