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Mix&Meal

1. Introduction

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The Vision : a product that recommends people dishes to cook based on what is available in their refrigerator.

The Problems:

1. Decision Fatigue: The question "What should I make for dinner?" can cause significant decision-making stress for busy people + wasting time.
2. Budgetary Constraints: People with tight budgets want to maximise how much they get out of their groceries?
3. Food Waste: People often throw away food that's gone bad, forgotten in the back of the fridge. Will the product aim to minimize food waste?
4. Lack of Creativity: People get stuck in ruts, making the same meals repeatedly. Will the product spark inspiration and suggest new uses for familiar ingredients?
5. Macro Tracking: People who track their macronutrients (protein, fat, carbs) may struggle to find recipes that align with their goals, especially when relying on what's on hand.

2. ICPs

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Who is the most affected?
The Budget Conscious:Users wanting to save money by maximizing what they already have.
Demographics
Age: 25-45: Young/Middle-aged professionals establishing careers, as well as those with potential families.
Location: Urban/suburban areas. Density matters for convenience value and those less likely to cook if commuting is difficult.
Income: Middle-income and above. They have some disposable income, but time is often a greater constraint than money.
Occupation: A wide range - office workers, teachers, healthcare, creatives... anyone having work schedules.
Family Status: Could be single, couples, or families. Family size impacts the kind of recipes that would be relevant.
Tech Savvy: They'll need to be comfortable with apps and online services.
JTBDs
The Main Job: Feed myself/my family affordably, making the most of what I buy.
Functional: Minimize spending at the grocery store, stretch ingredients into as many meals as possible.
Emotional: Reduce financial anxiety, feel resourceful and clever about managing food costs, avoid guilt about food seemingly 'going to waste'.
Social: May include the job of feeding others on a tight budget, and not wanting them to feel deprived.
Related "Jobs"
"Pantry Staples" focus: Prioritize recipes that rely on inexpensive, long-lasting ingredients a budget-conscious person likely has on hand.
Bulk ingredient usage: Suggest ways to use a large, economical purchase of an ingredient in different ways (big bag of rice, etc.).
Cost comparison: Is a recipe is particularly budget-friendly, for those hyper-aware of food costs.
Considerations
Regional cost variations: What's "budget-friendly" will vary by location.
Dietary restrictions: Vegan/vegetarian or allergy-friendly recipes may have different budget considerations.
The Busy Professional: Users short on time, needing quick meal ideas during the workweek.
Demographics
Age: Broad. Budget may be a concern at any age (students, retirees, etc.).
Location: Urban, suburban, and even rural. Economic circumstances will vary greatly in all locations.
Income: Lower- to lower-middle income.
Occupation: Students, Service industry, retail, part-time workers... or those impacted by job instability. Could overlap with parents who prioritize feeding a family on a budget.
Family Status: More likely to be shopping for more than just themselves.
Tech Considerations: Access to smartphones/data plans.
JTBDs
The Main Job: Feed myself/my family healthy, satisfying meals with minimal time and mental effort during the week.
Functional: Get food on the table quickly, no complex recipes.
Emotional: Reduce stress, avoid resorting to unhealthy takeout, feel like I'm providing proper nourishment even when short on time.
Social: May include the job of pleasing varying tastes in a household.
Related "Jobs"/could help with:
Minimizing grocery trips: Recipe suggestions focused on maximizing what's already available.
"Inspiration on autopilot": Generating enough variety to prevent boredom without time spent searching.
The Adventurous Eater: Users who like trying new dishes but feel overwhelmed by endless recipes online.
The Eco-Friendly Home Cook: Users who are passionate about sustainability and reducing food waste.
Fitness Enthusiasts: Users who are serious about workouts and body composition goals are on specific calorie or macro-focused diets for weight loss or health reasons.
People with Health Conditions: Users needing to manage macros related to conditions like diabetes or certain restrictive diets.

Focusing on Archetypes of ‘The Budget Conscious’ and ‘The Busy Professional’.

Important Considerations
Intersectionality: A person can be BOTH budget-conscious and a busy professional.
Sensitivity: Avoid stereotypes about who is or isn't budget-conscious. Messaging - empowering.

3. User Stories

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Archetype 1: Busy Professional
U1: As a busy professional who cooks at home 3-4 nights a week, I want to quickly see meal options based on the ingredients I already have in my fridge so I can make dinner without extra shopping trips. (Inventory Integration, Recipe Recommendation)
U2: As someone who is short on time after work, I want the app to suggest recipes that take 30 minutes or less to prepare, allowing me to get a healthy meal on the table without extra hassle. (Recipe Filtering based on Time)
U3: Since I'm often indecisive after a long day, I want the app to recommend 2-3 different recipe options each night to give me some variety and avoid meal prep monotony. (Multi-Recipe Recommendation)
U4: To save time on cleanup, I would appreciate the app suggesting recipes that require minimal pots and pans. (Recipe Attribute - Easy Cleanup - Optional)

Archetype 2: Budget-Conscious Shopper
U5: As someone who is on a tight budget, I want to find creative ways to use up all the ingredients I have in my fridge before they spoil, helping me save money on groceries. (Inventory Integration, Recipe Recommendation prioritizing use of expiring ingredients)
U6: I often buy ingredients on sale and end up with random things in my fridge. I want the app to suggest meals that incorporate these unexpected ingredients to avoid them going to waste. (Recipe Recommendation based on Unexpected Ingredients)
U7: While I'm open to trying new things, I'm also mindful of costs. I want the app to display an estimated cost per serving for each recipe so I can make informed choices. (Recipe Cost Estimation - Optional)
U8: Since I sometimes forget what's in my fridge, I want to be able to quickly scan barcodes of leftover ingredients to update my inventory and see what meals I can make. (Barcode Scanning Integration with Inventory Management)

4. Features

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Inventory Integration: A system for users to input their fridge contents (manual entry, barcode scanning for each item, grocery bill scanning- future iterations).
Onboarding (Personalisation): Let users choose cuisine preferences, available appliances, dietary restrictions, time constraints, and more.
Recipe Recommendation: Recommendation engine that match user preferences (dietary restrictions, time constraints, appliances available, cuisine preferences) with their available ingredients (from Inventory Integration) to generate/suggest a recipe. Prioritise using up soon-to-expire items for budget-conscious users. Offer recipe variations based on missing ingredients.
LLM-Enhanced Recipe Recommendation
Recipe Database:
Beyond titles and ingredients lists, LLMs can analyze full recipe text instructions for deeper understanding.
Semantic Matching:
LLM analyzes user inventory and preferences.
It then compares this against the recipe database, understanding nuances of ingredients and cooking techniques beyond simple keyword matching. Greater relevance.
Creative Substitutions:
LLM identifies potential substitutions if the user is missing a minor ingredient.
Flavor Exploration:
LLM identifies new or unexpected flavor combinations based on the user's inventory, going beyond standard recipe categories.
Recipe Ranking and Presentation
Relevance: Recipes are ranked by best match with user inputs, including LLM-identified substitutions and flavor profiles.
Personalization: Over time, the LLM can learn a user's favorite flavors and suggest those more frequently.
Recipe Presentation:
Clear step-by-step instructions that use the appliances and tools that the user has mentioned they have.
Additional info the LLM can pull out: potential allergens, suitable side dishes, interesting flavor notes, side dishes, etc.
Recipe Filtering: Allow users to filter recipes based on various criteria, including cook time (for Busy Professionals).
Multi-Recipe Recommendation: Generate a selection (2-3) of recipes each time for variety.
Recipe Attribute - Easy Cleanup: Integrate a tagging system for recipes with minimal cleanup requirements.
Recipe Cost Estimation: A system to estimate the cost per serving based on recipe ingredients and average grocery prices (may require integration with external data sources).

5. Non Functional Requirements

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Performance
Recipe Load Time: Recipes, including images and instructions, should load within 5 seconds of the user submitting a search or selecting a recipe from recommendations.
Search Responsiveness: Ingredient suggestions and filtering options (dietary, time, etc.) should display search results with minimal delay (under 3 seconds).
Offline Functionality: If feasible, users should be able to access a "favorites" list or basic pantry inventory even without an internet connection.

Scalability
Concurrent Users: The system should handle [X] concurrent users without significant performance degradation. (Start with a modest goal, and scale up as your user base grows).
Database Growth: Recipe and user data storage should be able to scale affordably and efficiently, accommodating at least [X number] of recipes and [X number] of users.
API Limits: Design the system to gracefully handle potential rate limits or changes in pricing from any external APIs for recipes or cost estimations.

6. Technical Specifications - Native Mobile App

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Architecture

Frontend:
Framework: React Native (Cross-platform development, strong performance)
State Management: Redux or MobX for managing complex application state
Navigation: React Navigation or similar solution
API Interaction: Axios or Fetch API
Backend:
Framework: Node.js(Scalable, good performance for I/O-bound tasks) or Python with Flask/Django
Database: PostgreSQL(Robust relational database, good for structured data)
Cloud Hosting: AWS
Search: Elasticsearch
Additional Architectural Considerations
Offline Storage: SQLite or Realm for storing inventory data locally
Push Notifications: Service like Firebase Cloud Messaging or AWS SNS (for features like expiring ingredient alerts, etc.)
Barcode Scanning: Mobile vision library compatible with React Native (if you intend to build it as a native app) or a third-party API integration
Recipe API / Scraping: Evaluate existing recipe providers with comprehensive data
LLM (Future Iteration): Explore options like GPT-J or specialized open-source culinary language models

Technologies

Programming Languages: JavaScript (Frontend, Backend), Python (Potential Backend Option), SQL (Database), Swift/Objective-C or Kotlin/Java (for native module development if needed)
Mobile Development Tools: Xcode (iOS Development), Android Studio (Android Development)
Cloud Services: (Same as web app, potentially adding mobile-focused services)
Mobile Analytics: Firebase, AppDynamics, etc., for tracking usage patterns
Additional Tools/Libraries:
Barcode Scanning: Native mobile vision library (platform-specific) or a third-party SDK
Testing Frameworks: Consider unit testing frameworks specific to your chosen platforms.

Considerations for Native Development

Cross-Platform Compatibility
Performance: mindful of complex UI interactions.
Hybrid Development: using a webview-based approach for parts of the app where native performance isn't critical to save development time.
Potential Native Modules: Some features (complex barcode scanning, etc.) may need native modules in Swift/Objective-C or Kotlin/Java.

7. Assumptions and Constraints

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Assumptions
Target User Tech Literacy: Users have a basic understanding of smartphone usage and app navigation.
Recipe API Availability: Existing recipe APIs will continue to provide sufficient data (ingredients, instructions, etc.) and remain cost-effective for your usage scale.
Initial Focus: The initial focus is on core functionality (recipe recommendations based on inventory), with advanced features added in later iterations.
Data Privacy: Users are generally aware of standard app data collection practices, and app will comply with relevant regional privacy regulations.
Constraints
Development Budget: Early on, financial resources for development may be limited. This will influence feature prioritization and technology choices.
Recipe Database: The initial recipe database might not be exhaustive, impacting the variety of recommendations available to early users.
Barcode Scanning Accuracy: Barcode scanning technology may have limitations, especially with unpackaged or damaged items.
LLM Integration: Incorporating Large Language Models can be computationally expensive and might require a staged approach.
Additional Considerations
User Adoption: The app's success will depend on achieving a critical mass of users and a compelling value proposition to attract them initially.
Evolving Technology: The app landscape is competitive, and new technologies or competitor features may shift priorities over time.

8. Risks and Mitigation Strategies:

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Limited Recipe Database: If users find the recipe selection too limited, they will become unengaged - goes against core value prop.
Mitigation:
Source multiple recipe APIs/DBs for greater coverage.
Encourage user-generated recipe submissions (with quality control).
Partner with recipe blogs or smaller sites for initial content population.
Be transparent with users about database growth in our messaging.
Competition: The recipe app market is crowded. Mix&Meal must offer a compelling differentiator.
Mitigation:
Emphasize unique focus on inventory utilization and waste reduction in market positioning.
Build an exceptional user experience (UI, UX - intuitive workflow).
Over time, personalization features and potential LLM integrations will create a technological moat.
User Adoption: Gaining a critical mass of users is crucial for ongoing success.
Mitigation:
Target marketing efforts at specific niche communities (eco-conscious home cooks, those on tight budgets, students abroad, etc.).
Incentivize early user referrals and reviews.
Partner with influencers in relevant spaces to gain exposure.
Technical Challenges: Accurate barcode scanning and complex LLM integration could prove difficult.
Mitigation:
Start with simple barcode scanning (common packaged goods), enhance over time.
Use smaller LLMs initially, test LLM-powered features in limited capacity before full-scale deployment.
Invest in developers with the necessary expertise or partner with those specializing in these areas.
Cost of Operation: Recipe APIs, LLM usage, and cloud hosting can become expensive as the user base grows.
Mitigation:
Explore tiered pricing – a free basic version, and premium for advanced features.
Creative partnerships for revenue (recipe sites, grocery stores).
Utilize cost-optimization techniques (serverless architecture, caching).
Continuously evaluate emerging cost-effective technologies.
Data Privacy Concerns: Users may be hesitant to share detailed information about their kitchen inventory.
Mitigation:
Develop a robust privacy policy and communicate it transparently.
Prioritize on-device data storage if possible.


9. Release Plan

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Proof of Concept (PoC)
Core Ingredient Input: Manual entry of ingredients in a simple search field.
Basic Recipe Filtering: Focus on pre-set filters for common dietary restrictions (vegetarian, gluten-free, etc.).
Recipe Database (Limited): Use a small, open-source recipe API to keep costs minimal. The goal is to show recipe generation, not perfection at this stage.

Minimum Viable Product (v0.1)
Expanded Ingredient Input: Add barcode scanning capabilities for at least the most common packaged items to increase ease of use.
Recipe Filtering 2.0: Include time-based filtering (under 30 minutes, etc.) and basic taste preferences ("Asian cuisines", "spicy", etc.).
Recipe Presentation: Invest in well-formatted recipe display, clear visuals, and basic instructions.
Inventory Management (Basic): Allow users to track which ingredients they've used, updating their virtual fridge.

Minimum Viable Product (v0.2)
Personalisation (Initial): Basic preference storage, so frequent users don't have to re-input dietary needs.
LLM Integration (Simplified): Explore smaller LLMs for tasks like suggesting simple substitutions if the user is missing something.
Inventory "Expiration Alerts": Reminders to use up perishables, reducing waste.

Phase 1: Proof of Concept (PoC)
Goal: Validate core concept, test basic tech feasibility.
Timeline: ~ 4 Sprints (Aim for 2 months or less)

Milestone 1: Basic Ingredient Input & Filtering
Sprint 1-2:
Setup development environment, backend, database, API connections.
Manual ingredient input, simple recipe search form.
Integrate with a test recipe API (small data set).
Filter by common dietary restrictions.

Milestone 2: Demo-Ready PoC
Sprint 3-4:
Minimal UI design (focus on function, not visual polish at this stage).
User can input a few ingredients and get relevant, viewable recipe results.
Internal demo and feedback for major technical pivot, if needed.
Publish Fake Door Landing Pages with Waitlist
Recruit ICP for validation and further customer development.

Phase 2: Minimum Viable Product (MVP)
Goal: Core functionality, ready for early adopters.
Timeline: ~ 8-10 Sprints (Estimate 4-5 months)

Milestone 3: Inventory System
Sprint 5-7:
Inventory creation and editing, link with recipe recommendations.
Basic pantry staples list.
Barcode scanning (for at least packaged items with clear barcodes).

Milestone 4: Recipe Presentation & User Experience
Sprint 8-10
Polished recipe display: images, clear instructions, estimated servings.
"Use Up Soon" feature: Alerts for expiring ingredients.
Recipe filtering by time, basic cuisine tags.
Continuously Test and Iterate based on Feedback.

Phase 3: Expanded Features (Beyond MVP)
Goal: Differentiation and increased user retention
Timeline: Ongoing sprints, prioritized based on user feedback.
Grocery Bill Scanning - Making it easier to bulk input inventory.
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