Document Version: 1.0.0 Last Updated: 2026-01-21 Product Owner: Dang Phung Status: Active Development
- Executive Summary
- Product Overview
- Goals and Objectives
- Target Market & User Personas
- User Stories & Use Cases
- Feature Requirements
- User Experience Requirements
- Technical Requirements
- Success Metrics
- Risks & Mitigations
- Dependencies
- Development Phases
- Appendix
Modern life presents an overwhelming number of choices, leading to decision fatigue and analysis paralysis. People waste hours overthinking simple decisions while sometimes rushing through important ones. Existing solutions are either too simplistic (coin flips, random pickers) or too complex (spreadsheets, decision matrices), and none leverage AI to provide personalized, context-aware guidance.
Solus is an AI-powered Progressive Web App (PWA) that helps users make better decisions through intelligent guidance and personalized recommendations. It provides two distinct modes:
- Quick Decision Mode: Voice-powered 30-second flow for everyday choices
- Deep Reflection Mode: Multi-step guided framework for complex, life-impacting decisions
The app learns from user patterns, provides judgment-free support, and helps users build better decision-making skills over time through the Decision Journal feature.
Freemium SaaS
- Free Tier: Limited daily quick decisions (10/day), basic deep reflections (3/month)
- Premium Tier ($9.99/month or $89.99/year): Unlimited decisions, enhanced AI insights, decision journal with analytics, priority support
- No ads, no data selling - Privacy-first approach
- Voice-First Interface: Natural speech input eliminates typing friction
- Dual-Mode Approach: Appropriate depth for both trivial and critical decisions
- Learning System: Improves recommendations based on user patterns and outcomes
- Decision Journal: Tracks outcomes and builds personal decision intelligence
- Privacy-Focused: Judgment-free space for sensitive decisions
- Ethical AI: Clear boundaries for medical/legal topics with appropriate disclaimers
To become the world's leading AI-powered decision assistant, helping millions overcome decision fatigue and make better choices through intelligent, personalized guidance.
Empower people to make confident, well-reasoned decisions by providing AI-driven insights, structured frameworks, and personalized recommendations that learn from their unique patterns and values.
Progressive Web App (PWA) with native-like experience across:
- Mobile browsers (iOS Safari, Android Chrome)
- Desktop browsers (Chrome, Firefox, Safari, Edge)
- Installable to home screen (PWA install)
- Offline-capable with service workers
| User Pain Point | Solus Solution |
|---|---|
| Decision fatigue from too many choices | AI-powered recommendations reduce cognitive load |
| Overthinking simple decisions | Quick Decision mode provides instant clarity |
| Missing important considerations | AI surfaces blind spots and overlooked factors |
| Lack of confidence in choices | Structured frameworks validate thinking |
| Time wasted on trivial decisions | 30-second voice flow accelerates process |
| Difficulty with complex decisions | Deep Reflection mode provides step-by-step guidance |
| No learning from past decisions | Decision Journal tracks outcomes and patterns |
-
User Acquisition
- Achieve 10,000 active users within 6 months of MVP launch
- Reach 100,000 active users within 18 months
- Convert 5% of free users to premium within first 3 months
-
Engagement
- 40% Day-7 retention rate
- 25% Day-30 retention rate
- Average 5 decisions per user per week
-
Revenue
- Generate $10,000 MRR within 12 months
- Achieve $100,000 MRR within 24 months
- Maintain <$5 CAC (Customer Acquisition Cost)
-
Functionality
- Complete MVP with Quick Decision mode
- Ship Decision Journal feature in MVP
- Launch Deep Reflection mode in V2
- Achieve 95%+ AI recommendation accuracy
-
Quality
- Maintain 99.5% uptime
- <2 second response time for AI recommendations
- 90%+ test coverage for critical features
- Lighthouse PWA score >90
-
User Satisfaction
- Net Promoter Score (NPS) >50
- 4.5+ star rating on app stores
- <5% churn rate for premium users
-
80% decision completion rate
-
Young Professionals (25-35 years old)
- Tech-savvy millennials with disposable income
- Experience decision fatigue from work and personal life
- Value time efficiency and personal growth
-
Career-Focused Individuals (30-45 years old)
- Busy professionals making frequent decisions
- Need to optimize decision-making time
- Face complex career and life choices
-
Wellness-Oriented Users (22-40 years old)
- Gen Z and millennials focused on mental health
- Struggle with analysis paralysis and overthinking
- Seek structured tools for self-improvement
Demographics
- Age: 28
- Occupation: Marketing Manager
- Location: Urban area (San Francisco)
- Income: $85,000/year
Background
- Works 50+ hours/week at a fast-paced tech company
- Makes hundreds of small decisions daily (what to eat, what to wear, which project to prioritize)
- Experiences decision fatigue by evening
- Uses productivity apps and wellness tools
Goals
- Reduce mental load from trivial decisions
- Make better work-life balance choices
- Feel more confident in daily decisions
- Save time on routine choices
Pain Points
- Wastes 30+ minutes deciding what to eat for lunch
- Overthinks weekend plans instead of enjoying free time
- Second-guesses career decisions
- Feels paralyzed by too many options
How Solus Helps
- Quick Decision mode for daily choices (food, entertainment)
- Voice input while commuting or multitasking
- AI learns lunch preferences and suggests based on context
- Decision Journal tracks what choices led to satisfaction
Quote: "I wish I could just speak my dilemma and get a smart recommendation without thinking about it for hours."
Demographics
- Age: 34
- Occupation: Software Engineer
- Location: Suburban area (Austin, TX)
- Income: $120,000/year
Background
- Analytical thinker who researches decisions extensively
- Facing major life decisions (job offers, relocation, relationships)
- Uses spreadsheets and lists to compare options
- Values data-driven insights
Goals
- Make well-reasoned decisions for important life choices
- Ensure all factors are considered
- Reduce bias in decision-making
- Have confidence in final choices
Pain Points
- Spends weeks comparing options for big decisions
- Struggles to weigh emotional vs. practical factors
- Fears missing important considerations
- Analysis paralysis prevents action
How Solus Helps
- Deep Reflection mode guides through complex decisions
- AI identifies cognitive biases
- Structured framework ensures comprehensive analysis
- Values alignment helps balance head vs. heart
Quote: "I need something more sophisticated than a pros/cons list but less overwhelming than my 10-tab spreadsheet."
Demographics
- Age: 24
- Occupation: Graphic Designer (Freelance)
- Location: Mid-size city (Portland, OR)
- Income: $45,000/year
Background
- Creative professional with flexible schedule
- Struggles with decision-making in all areas
- Seeks validation and external input for choices
- Active on social media and wellness communities
Goals
- Build confidence in decision-making abilities
- Stop relying on others to decide for her
- Learn from past decisions
- Make choices that align with personal values
Pain Points
- Asks friends for opinions on every decision
- Regrets choices after making them
- Doesn't trust own judgment
- Switches decisions frequently
How Solus Helps
- AI provides neutral, judgment-free guidance
- Decision Journal shows patterns in what works
- Values assessment builds self-awareness
- Track outcomes to build confidence over time
Quote: "I always ask my friends what I should do. I wish I could trust myself more."
As a busy professional I want to speak my decision aloud instead of typing So that I can make quick decisions while multitasking
Acceptance Criteria
- User can tap microphone button to start voice recording
- Speech-to-text accurately transcribes decision and options
- AI extracts decision title, options, and context from speech
- User can review and edit AI-extracted information
- Works in noisy environments with reasonable accuracy
Use Case
- Sarah is walking to a lunch meeting
- She opens Solus and taps the microphone
- She says: "Should I get the salad or the burrito bowl for lunch? I'm trying to eat healthy but I'm really hungry and the burrito sounds good."
- AI extracts: Decision: "Lunch choice", Options: ["Salad", "Burrito bowl"], Context: ["Trying to eat healthy", "Very hungry"]
- Sarah reviews the extracted info, confirms it's correct
- AI provides recommendation with reasoning
As a user making a quick decision I want to receive an AI-powered recommendation with clear reasoning So that I can understand why one option is suggested
Acceptance Criteria
- AI analyzes decision title, options, and context
- Recommendation is provided within 3 seconds
- Reasoning explains the recommendation clearly
- Confidence level is displayed (e.g., "80% confident")
- User can accept or reject the recommendation
Use Case
- Marcus enters decision: "Which framework for side project?"
- Options: React, Vue, Svelte
- Context: "Need fast development, familiar with React, want to learn something new"
- AI recommends: "Svelte"
- Reasoning: "Svelte has a gentle learning curve and faster build times, aligning with your goal to learn while maintaining productivity."
- Marcus reads reasoning and accepts recommendation
As a user I want to view my past decisions So that I can reference previous choices and learn from them
Acceptance Criteria
- All decisions are automatically saved
- Decision history is accessible from dashboard
- Decisions can be filtered by category, date, status
- User can search decision history
- Outcomes can be logged for each decision
Use Case
- Emma opens Solus dashboard
- She sees list of past decisions sorted by date
- She filters to "Food" category
- She notices she always chooses Thai food on Fridays
- She updates a decision with outcome: "Good choice, felt satisfied"
As a user facing a complex decision I want to be guided through a structured reflection process So that I can thoroughly evaluate all aspects
Acceptance Criteria
- User selects "Deep Reflection" mode
- AI provides step-by-step prompts
- Each step focuses on specific aspect (values, stakeholders, consequences, etc.)
- User can save progress and return later
- Final summary synthesizes all inputs
Use Case
- Marcus faces job offer decision (current company vs. new offer)
- He selects Deep Reflection mode
- Step 1: AI asks about personal values - he prioritizes learning, work-life balance, impact
- Step 2: AI prompts for stakeholder impact - considers family, finances, career growth
- Step 3: AI guides through pros/cons for each option
- Step 4: AI explores future scenarios for each choice
- Step 5: AI identifies potential biases (status quo bias)
- Step 6: Final recommendation with comprehensive reasoning
As a user I want to align my decision with my personal values So that I make choices that reflect what matters most to me
Acceptance Criteria
- User can define or select personal values
- AI assesses how each option aligns with stated values
- Visual representation shows alignment scores
- Values are saved for future decisions
- User can update values over time
Use Case
- Emma is deciding between two freelance projects
- She identifies values: Creativity, Financial stability, Flexibility
- Project A: High-paying, strict deadlines, corporate work
- Project B: Lower pay, creative freedom, flexible schedule
- AI shows alignment: A (70% financial, 30% creativity), B (90% creativity, 80% flexibility)
- Emma sees Project B better aligns with 2 of 3 top values
- She chooses Project B with confidence
As a user I want to track the outcomes of my decisions So that I can learn what types of choices work well for me
Acceptance Criteria
- User receives follow-up prompts after decision (configurable timing)
- User can rate decision outcome (1-5 stars)
- User can add notes about what happened
- Patterns are identified across decision outcomes
- Insights are shown on personal dashboard
Use Case
- Sarah decided to try a new restaurant (Solus recommended it)
- 2 days later, Solus prompts: "How was your experience at [Restaurant]?"
- Sarah rates 5 stars and notes: "Great food, perfect for date night"
- Over time, Solus learns Sarah values ambiance for dinner dates
- Future restaurant recommendations factor in this preference
As a user I want to see patterns in my decision-making So that I can understand my preferences and improve future choices
Acceptance Criteria
- Dashboard shows decision patterns (categories, frequency, outcomes)
- Insights are generated from aggregated data
- Visualizations show trends over time
- Recommendations improve based on learned patterns
- User can see what types of decisions they struggle with
Use Case
- Marcus views his Decision Journal after 3 months
- Dashboard shows: "You made 45 decisions this quarter"
- Insight: "You're happiest with decisions made quickly (average satisfaction: 4.2/5)"
- Pattern: "Career decisions made over >2 weeks have lower satisfaction (3.5/5)"
- Recommendation: "Consider setting decision deadlines to avoid overthinking"
- Marcus adjusts his approach based on personal data
As a new user I want to quickly understand how Solus works So that I can start making better decisions immediately
Acceptance Criteria
- Simple sign-up flow (email or Google OAuth)
- Interactive tutorial demonstrates Quick Decision mode
- Sample decision walkthrough shows AI capabilities
- User can skip tutorial if desired
- Onboarding completes in <2 minutes
As a frequent user I want to upgrade to premium for unlimited access So that I can use Solus without daily limits
Acceptance Criteria
- Clear comparison of Free vs. Premium features
- Seamless payment flow (Stripe integration)
- Instant access to premium features after payment
- Monthly and annual billing options
- Easy cancellation process
Functional Requirements
- FR-1.1: Users can sign up with email and password
- FR-1.2: Users can sign in with Google OAuth
- FR-1.3: Users can reset password via email
- FR-1.4: Users can update profile information
- FR-1.5: Users can delete their account and all data
Non-Functional Requirements
- NFR-1.1: Authentication must use Firebase Auth
- NFR-1.2: Passwords must be encrypted with bcrypt
- NFR-1.3: Session tokens expire after 30 days
- NFR-1.4: Account deletion must comply with GDPR (permanent deletion)
Functional Requirements
- FR-2.1: Users can input decision via voice (speech-to-text)
- FR-2.2: Users can input decision via text (manual typing)
- FR-2.3: AI extracts decision title, options, and context from input
- FR-2.4: Users can review and edit AI-extracted information
- FR-2.5: AI generates recommendation with reasoning
- FR-2.6: Users can accept, reject, or modify recommendation
- FR-2.7: Decision is automatically saved to history
- FR-2.8: Users can categorize decisions (Food, Entertainment, Career, etc.)
- FR-2.9: Users can add manual context factors
- FR-2.10: Users can input "gut feeling" for AI to consider
Non-Functional Requirements
- NFR-2.1: Speech-to-text accuracy >90% in quiet environments
- NFR-2.2: AI recommendation generated in <3 seconds
- NFR-2.3: Voice input supports English (initial release)
- NFR-2.4: AI uses Gemini Flash model for speed
- NFR-2.5: Recommendations must include confidence level
Technical Implementation
- Web Speech API for speech-to-text
- Gemini Flash AI model for extraction and recommendation
- Zod schemas for structured output validation
- Firestore for decision storage
Functional Requirements
- FR-3.1: All decisions automatically saved to user's journal
- FR-3.2: Users receive follow-up prompts (configurable timing)
- FR-3.3: Users can rate decision outcomes (1-5 stars)
- FR-3.4: Users can add notes about decision results
- FR-3.5: Dashboard displays decision history
- FR-3.6: Decisions can be filtered by category, date, outcome
- FR-3.7: Search functionality for decision history
- FR-3.8: Insights generated from decision patterns
Non-Functional Requirements
- NFR-3.1: Follow-up prompts sent via in-app notifications
- NFR-3.2: Decision history loads in <1 second
- NFR-3.3: Pattern recognition runs weekly via scheduled job
- NFR-3.4: Insights updated daily
Technical Implementation
- Firebase Cloud Functions for scheduled follow-ups
- Firestore queries for filtering and search
- Pattern recognition algorithm analyzes outcomes
- Dashboard visualizations with recharts library
Functional Requirements
- FR-4.1: App installable to home screen (iOS, Android, Desktop)
- FR-4.2: App works offline (view past decisions)
- FR-4.3: App caches AI responses for offline access
- FR-4.4: App syncs data when connection restored
- FR-4.5: App shows connection status indicator
Non-Functional Requirements
- NFR-4.1: Lighthouse PWA score >90
- NFR-4.2: Service worker caches critical assets
- NFR-4.3: Offline functionality gracefully degrades
- NFR-4.4: App size <5MB (initial bundle)
- NFR-4.5: Works on Chrome, Safari, Firefox, Edge
Technical Implementation
- vite-plugin-pwa for service worker generation
- Workbox for caching strategies
- IndexedDB for offline data storage
Functional Requirements
- FR-5.1: UI optimized for mobile devices (320px - 768px)
- FR-5.2: UI adapts to tablet (768px - 1024px)
- FR-5.3: UI scales for desktop (1024px+)
- FR-5.4: Touch-friendly buttons and inputs (44px minimum)
- FR-5.5: Accessible keyboard navigation
Non-Functional Requirements
- NFR-5.1: Mobile-first design approach
- NFR-5.2: Touch targets meet WCAG 2.1 guidelines
- NFR-5.3: Responsive images with lazy loading
- NFR-5.4: Supports portrait and landscape orientations
Functional Requirements
- FR-6.1: Multi-step guided framework for complex decisions
- FR-6.2: Values alignment assessment
- FR-6.3: Stakeholder impact analysis
- FR-6.4: Weighted pros/cons comparison
- FR-6.5: Future scenario visualization
- FR-6.6: Cognitive bias identification
- FR-6.7: Save progress and resume later
- FR-6.8: Final synthesis and recommendation
Non-Functional Requirements
- NFR-6.1: Uses Gemini Pro model for deeper analysis
- NFR-6.2: Complete framework in 10-15 minutes
- NFR-6.3: Progress auto-saved every 30 seconds
Functional Requirements
- FR-7.1: Pre-built templates for common decisions
- FR-7.2: Users can save custom templates
- FR-7.3: One-tap decision from saved template
- FR-7.4: Template categories (Food, Entertainment, Shopping, etc.)
- FR-7.5: Share templates with other users (optional)
Functional Requirements
- FR-8.1: Subscription management (upgrade, downgrade, cancel)
- FR-8.2: Unlimited quick decisions (vs. 10/day free)
- FR-8.3: Unlimited deep reflections (vs. 3/month free)
- FR-8.4: Advanced analytics dashboard
- FR-8.5: Priority AI model access (faster responses)
- FR-8.6: Export decision data (CSV, JSON)
- FR-8.7: Premium-only templates
Non-Functional Requirements
- NFR-8.1: Payment processing via Stripe
- NFR-8.2: Subscription status checked on each request
- NFR-8.3: Grace period for failed payments (7 days)
- NFR-8.4: Pro-rated refunds for annual subscriptions
Functional Requirements
- FR-9.1: Cross-user pattern identification (anonymized)
- FR-9.2: Seasonal and contextual awareness
- FR-9.3: Preference prediction based on history
- FR-9.4: Mood-based recommendations
- FR-9.5: Time-of-day preferences
Functional Requirements
- FR-10.1: Calendar integration (Google Calendar, Apple Calendar)
- FR-10.2: Task manager integration (Todoist, Notion)
- FR-10.3: Weather-based context (for location decisions)
- FR-10.4: Smart home integration (optional)
Functional Requirements
- FR-11.1: Share decision with friends/family for input
- FR-11.2: Group decision mode with multiple voters
- FR-11.3: Consensus building tools
- FR-11.4: Anonymous voting option
- Simplicity: Every screen should have one primary action
- Speed: Users should complete quick decisions in <30 seconds
- Clarity: AI reasoning must be understandable to average users
- Delight: Micro-interactions provide positive feedback
- Trust: Transparent about AI limitations and data usage
1. User opens app → Dashboard
2. Taps "Quick Decision" button
3. Taps microphone icon
4. Speaks decision aloud
5. Reviews AI-extracted info
6. Edits if needed (optional)
7. Taps "Get Recommendation"
8. Reviews AI recommendation
9. Accepts or customizes
10. Decision saved automatically
Time to complete: <30 seconds
1. User opens app → Dashboard
2. Taps "Quick Decision" button
3. Enters decision title manually
4. Adds options (2-5)
5. Adds context (optional)
6. Taps "Get Recommendation"
7. Reviews AI recommendation
8. Accepts or customizes
9. Decision saved automatically
Time to complete: 1-2 minutes
1. User opens app → Dashboard
2. Scrolls to "Recent Decisions" section
3. Taps "View All"
4. Sees list of past decisions
5. Filters by category (optional)
6. Taps on a decision to view details
7. Sees original decision, AI recommendation, and outcome (if logged)
Time to complete: 10-30 seconds
1. User receives notification: "How did your decision turn out?"
2. Taps notification → Opens decision detail
3. Rates outcome (1-5 stars)
4. Adds optional notes
5. Submits feedback
6. Returns to dashboard
Time to complete: <1 minute
Colors
- Primary: Solus Purple (#7C3AED)
- Secondary: Soft Blue (#60A5FA)
- Success: Green (#10B981)
- Warning: Yellow (#F59E0B)
- Error: Red (#EF4444)
- Neutral: Gray scale (#F9FAFB to #111827)
Typography
- Headings: Inter Bold
- Body: Inter Regular
- Mono: Jetbrains Mono (for code)
Components
- Use shadcn/ui component library
- Mobile-first responsive design
- Touch targets minimum 44x44px
- Consistent spacing (4px, 8px, 16px, 24px, 32px)
Animations
- Micro-interactions for button presses (scale, ripple)
- Smooth transitions (200-300ms)
- Loading states with skeleton screens
- Success animations for completed actions
Frontend
- React 19 with TypeScript
- Vite for build tooling
- Tailwind CSS v3 for styling
- shadcn/ui for component library
- Vertical slice architecture (feature-based organization)
Backend
- Firebase Authentication
- Firestore (NoSQL database)
- Firebase Cloud Functions (serverless)
- Firebase Storage (future: audio recordings, exports)
AI/ML
- Google Gemini AI (via Vercel AI SDK)
- Gemini Flash latest for quick decisions
- Gemini Pro latest for deep reflections
- Zod for schema validation
- Structured output with generateObject()
- Graceful fallbacks for AI failures
State Management
- React Context API for auth and theme
- Local state (useState) for component state
- Zustand (optional, for future complex state)
Testing
- Vitest for unit and integration tests
- React Testing Library for component tests
- 90%+ coverage for critical features
- Functional/integration tests preferred
CI/CD
- GitHub Actions for automated testing
- Code quality checks (ESLint, TypeScript)
- Automated releases with Release Please
- Dependabot for dependency updates
Hosting
- Vercel or Firebase Hosting for frontend
- Firebase Cloud Functions for backend
Database
- Firestore collections:
users/- User profiles and settingsquickDecisions/- Quick decision datadeepDecisions/- Deep reflection datareflections/- Post-decision reflectionstemplates/- Decision templates (future)subscriptions/- Premium subscription data (future)
API Keys Required
- Firebase (Auth, Firestore, Functions)
- Google Gemini API
- Stripe (for payments, Phase 2)
Performance Requirements
- Page load time: <2 seconds (3G connection)
- Time to Interactive (TTI): <3 seconds
- First Contentful Paint (FCP): <1.5 seconds
- Lighthouse score: >90 for all metrics
- AI response time: <3 seconds for quick decisions
- AI response time: <5 seconds for deep reflections
Security Requirements
- HTTPS only (enforced)
- Firebase Security Rules for data access
- API keys stored in environment variables
- Input sanitization for XSS prevention
- Rate limiting for AI API calls (100/user/day)
- GDPR compliance (data export, deletion)
Scalability
- Support 10,000 concurrent users (Phase 1)
- Support 100,000 concurrent users (Phase 2)
- Firestore auto-scaling
- Cloud Functions auto-scaling
- CDN for static assets
Monitoring & Analytics
- Firebase Analytics for usage tracking
- Sentry for error tracking
- Google Analytics for web analytics (optional)
- Custom dashboard for business metrics
Decisions Completed Per Week Per User
This metric indicates user engagement and value derived from the product.
Target: 5 decisions/user/week (MVP), 10 decisions/user/week (V2)
| Metric | Target (MVP) | Target (V2) | Measurement |
|---|---|---|---|
| Decision Completion Rate | 80% | 85% | % of started decisions that are finished |
| Average Decision Time (Quick) | <30 seconds | <20 seconds | Time from input to recommendation |
| Average Decision Time (Deep) | N/A (not in MVP) | 10-15 minutes | Time to complete full framework |
| AI Recommendation Accuracy | 85% | 90% | % of recommendations user agrees with |
| User Satisfaction (NPS) | 40 | 60 | Net Promoter Score |
| Metric | Target (MVP) | Target (V2) | Measurement |
|---|---|---|---|
| Day-1 Retention | 60% | 70% | % of users who return day after signup |
| Day-7 Retention | 40% | 50% | % of users active 7 days after signup |
| Day-30 Retention | 25% | 35% | % of users active 30 days after signup |
| Weekly Active Users (WAU) | 5,000 | 50,000 | Unique users active in past 7 days |
| Monthly Active Users (MAU) | 10,000 | 100,000 | Unique users active in past 30 days |
| Metric | Target (MVP) | Target (V2) | Measurement |
|---|---|---|---|
| Free to Premium Conversion | 3% | 5% | % of free users who upgrade |
| Monthly Recurring Revenue (MRR) | $1,000 | $10,000 | Total recurring revenue per month |
| Customer Acquisition Cost (CAC) | <$10 | <$5 | Cost to acquire one user |
| Customer Lifetime Value (LTV) | >$50 | >$100 | Revenue per user over lifetime |
| LTV:CAC Ratio | >3:1 | >10:1 | Ratio of lifetime value to acquisition cost |
| Churn Rate | <10% | <5% | % of premium users who cancel |
| Metric | Target | Measurement |
|---|---|---|
| Uptime | 99.5% | % of time service is available |
| Page Load Time | <2 seconds | Time to fully load page |
| API Response Time | <500ms | Average API response time |
| AI Response Time | <3 seconds | Time to generate AI recommendation |
| Test Coverage | >90% | % of code covered by tests |
| Lighthouse PWA Score | >90 | Google Lighthouse score |
- In-app feedback: "Was this recommendation helpful?" (thumbs up/down)
- Follow-up surveys: Email surveys to random sample of users monthly
- App store reviews: Monitor ratings and reviews on iOS/Android
- Support tickets: Track common issues and feature requests
- User interviews: Conduct 10 interviews per quarter
| Risk | Impact | Probability | Mitigation |
|---|---|---|---|
| AI API rate limiting or costs exceed budget | High | Medium | Implement caching, rate limiting, fallback to simpler models |
| Firebase costs scale unexpectedly | High | Medium | Monitor costs daily, optimize queries, set spending alerts |
| Speech-to-text accuracy too low | Medium | Medium | Provide manual text input option, test in various environments |
| PWA compatibility issues on iOS | Medium | Low | Extensive testing on Safari, provide fallback web app |
| Security vulnerability exposed | High | Low | Regular security audits, automated scanning, bug bounty program |
| Risk | Impact | Probability | Mitigation |
|---|---|---|---|
| Low user adoption | High | Medium | Extensive user testing, iterate on onboarding, referral program |
| Users don't convert to premium | High | Medium | Clearly differentiate free vs. premium, A/B test pricing |
| Competitors launch similar product | Medium | High | Focus on unique value (voice, journal, quality), build brand |
| Users don't trust AI recommendations | High | Low | Transparent reasoning, allow user override, build trust gradually |
| Legal issues with decision guidance | Medium | Low | Clear disclaimers, boundaries for medical/legal topics |
| Risk | Impact | Probability | Mitigation |
|---|---|---|---|
| Users don't complete decisions | High | Medium | Simplify flow, reduce steps, save progress automatically |
| AI recommendations perceived as low quality | High | Medium | Continuous model improvement, user feedback loop, A/B testing |
| Feature bloat makes app complex | Medium | Medium | Stick to core value, ruthlessly prioritize, user testing |
| Decision Journal low adoption | Medium | Medium | Automated follow-ups, show value early, gamification |
| Voice input not used | Low | Medium | Educate users on benefits, make it prominent, optimize UX |
Critical (Required for MVP)
- Firebase (Auth, Firestore, Hosting)
- Google Gemini AI API
- Vercel AI SDK
- Web Speech API (browser native)
Important (Required for V2)
- Stripe (payment processing)
- Email service (SendGrid or similar)
- Analytics platform (Google Analytics or Mixpanel)
Nice-to-have (Future)
- Calendar APIs (Google, Apple)
- Task manager APIs (Todoist, Notion)
- Weather API
- SMS notifications (Twilio)
Team Dependencies
- Design team: UI/UX mockups, branding, illustrations
- Development team: Frontend, backend, AI integration
- QA team: Testing, bug tracking
- Marketing team: Go-to-market strategy, content
Milestone Dependencies
- Phase 1 (MVP) must complete before Phase 2 begins
- User testing must validate MVP before V2 feature development
- Premium features require payment integration (Stripe setup)
Goal: Launch functional product with core Quick Decision mode
Features
- ✅ User authentication (email, Google OAuth)
- ✅ Quick Decision mode (voice and text input)
- ✅ AI recommendation engine (Gemini Flash)
- ✅ Decision Journal with automatic saving
- ✅ Follow-up prompts and outcome tracking
- ✅ Responsive design (mobile-first PWA)
- ✅ Basic analytics dashboard
Success Criteria
- 1,000 beta users
- 70% decision completion rate
- 30% Day-7 retention
- <3 second AI response time
Timeline: 3 months
Goal: Add Deep Reflection mode and premium features
Features
- Deep Reflection mode with multi-step framework
- Values alignment assessment
- Cognitive bias identification
- Quick Templates Library
- Premium subscription (Stripe integration)
- Advanced analytics dashboard
- Enhanced AI (Gemini Pro for deep decisions)
Success Criteria
- 10,000 active users
- 5% free-to-premium conversion
- $5,000 MRR
- NPS >50
Timeline: 3 months
Goal: Scale product with integrations and collaborative features
Features
- Advanced pattern learning
- Calendar integration
- Task manager integration
- Collaborative decision-making
- Mood and context awareness
- Visual decision maps
- Plugin/extension system
Success Criteria
- 100,000 active users
- $50,000 MRR
- 50% Day-30 retention
- NPS >60
Timeline: 6 months
- Decision: A choice between two or more options that a user needs to make
- Quick Decision: Simple, everyday choice resolved in <30 seconds
- Deep Reflection: Complex decision requiring structured analysis
- Decision Journal: Historical record of all user decisions and outcomes
- Follow-up: Prompt sent to user after decision to track outcome
- Pattern: Recurring preference or behavior identified from decision history
- Values Alignment: Matching decision options to user's stated values
- Cognitive Bias: Mental shortcut that may lead to suboptimal decisions
- Recommendation: AI-suggested option with reasoning
- Conventional Commits Specification
- Firebase Documentation
- Gemini AI Documentation
- Vercel AI SDK
- React Documentation
- Tailwind CSS Documentation
- shadcn/ui Documentation
| Version | Date | Author | Changes |
|---|---|---|---|
| 1.0.0 | 2026-01-21 | Claude AI | Initial PRD creation |
Document End