SeasonSense
Executive Summary
Vision Statement
Democratizing precision cooking by making professional-level seasoning accessible regardless of biological taste limitations.
Problem Summary
Home cooks with taste impairments struggle to season food appropriately, leading to social embarrassment and wasted meals. Medical conditions like zinc deficiency, thyroid disorders, or post-COVID parosmia can disrupt salt perception, causing users to overseason despite best intentions.
Proposed Solution
A smart kitchen ecosystem with sensor-enabled scales and AI-driven seasoning guidance that calibrates to individual taste profiles. The system measures actual sodium content and provides real-time feedback through a mobile interface.
Market Analysis
Target Audience
Home cooks experiencing taste distortion from:
- Post-COVID smell/taste disorders (affects 28M+ Americans with partial/full impairment)
- Thyroid conditions
- Zinc deficiency
- Medication side effects
- Chronic sinus issues
Niche Validation
Validated by high-engagement Reddit thread (191 upvotes, 119 comments) where multiple users reported identical seasoning struggles. Medical literature confirms taste distortion can persist for months post-COVID, with 24% experiencing partial smell recovery and 20% partial taste recovery.
Google Trends Keywords
Market Size Estimation
5.6M potential US users with chronic chemosensory disorders
1.2M early adopters actively seeking cooking solutions
Global smart kitchen market ($7.4B by 2025)
Competitive Landscape
No direct competitors addressing taste calibration:
- Smart scales (e.g., Escali) measure weight but not flavor
- Recipe apps lack sensory feedback
- Medical solutions focus on treatment not compensation
Product Requirements
User Stories
As someone with taste impairment, I need real-time salt concentration measurements so I don't overseason food
As a health-conscious user, I want sodium tracking per meal to maintain dietary goals
As an inconsistent cook, I need AI-powered seasoning suggestions based on dish type
MVP Feature Set
Bluetooth-connected precision scale with sodium sensor
Mobile app with taste calibration wizard
Real-time seasoning guidance overlay
Dish-specific seasoning profiles database
Non-Functional Requirements
Sensor accuracy: ±0.1g salt detection
Sub-500ms latency for real-time feedback
Offline functionality for kitchen environments
Key Performance Indicators
User retention rate (target > 70% at 90 days)
Recipe success score (user-reported seasoning accuracy)
Daily active device usage rate
Data Visualizations
Visual Analysis Summary
Recovery patterns from smell/taste disorders show significant long-term impairment rates, creating sustained market need
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Go-to-Market Strategy
Core Marketing Message
Cook confidently again. SeasonSense adapts to your unique taste perception, ensuring perfectly seasoned dishes every time.
Initial Launch Channels
- Targeted communities: r/Cooking, Long COVID support groups
- Cooking influencer partnerships (YouTube chefs)
- ENT clinic waiting room demos
Strategic Metrics
Problem Urgency
Critical
Solution Complexity
Medium
Defensibility Moat
Proprietary taste calibration algorithms and user-specific seasoning databases create switching costs. Hardware-software integration creates technical barriers.
Source Post Metrics
Business Strategy
Monetization Strategy
Freemium SaaS model:
- Base hardware: $129 sensor scale
- Subscription: $8/month for AI seasoning profiles
- Enterprise: $499 chef kits with multi-user calibration
Financial Projections
Year 1: $240K MRR (5K subscribers + hardware) Year 3: $1.2M MRR (25K subscribers + B2B partnerships)
Tech Stack
Python FastAPI for machine learning model serving and data processing
Time-series database (InfluxDB) for sensor data + PostgreSQL for user profiles
React Native for cross-platform mobile app with real-time feedback dashboard
OpenAI API for natural language cooking guidance, AWS IoT Core for device management, Stripe for payments
Risk Assessment
Identified Risks
- Medical condition variability affecting calibration accuracy
- Food texture interference with sensor readings
Mitigation Strategy
- Multi-point calibration with control substances
- Machine learning compensation for viscosity/density variables