CASE STUDY · HEALTH TECH & DATA INTELLIGENCE
MQP: The "Operator" System
How we built a full-stack health operating system and a custom market intelligence engine to validate the brand in just 4 days.
Tech Spec / RFP Answer
How does this compare to hiring a dev agency?
A traditional software agency would quote this as a "Custom Health App MVP," typically costing $50,000 to $80,000 with a 3-6 month timeline. They would focus heavily on backend infrastructure you might not need yet.
Fairway’s approach is a Rapid Validation Sprint:
- We strip away the heavy backend requirements and build a high-fidelity functional frontend (React/Vite) that feels real to the user and investors.
- For the MQP build, specifically, the cost was a flat sprint fee (roughly 15% of a traditional agency quote).
- Instead of waiting months for a database architect, we used lightweight data tools (CSV, Python Scripts, LocalStorage) to simulate a fully working product in 4 days.
- This allows you to validate the brand, the "Operator" lens, and the market appetite before raising capital or hiring a full engineering team.
Project Type
Full-Stack Product Build: React Application + Python Data Pipeline + Analytics Dashboard.
The "Lens"
Shifted from "Wellness Influencer" to "High-Performance Operator." Dark mode, terminal aesthetics, data-first.
Intelligence
Leveraged Google Programmable Search to track "Blueprint" vs. "MQP" sentiment across Reddit & LinkedIn.
The Challenge
The health space is crowded with "soft" wellness advice. The client needed a platform that spoke to Operators and Fathers—men who run companies and families and need a system, not a suggestion.
- Needed a "System" aesthetic: Dark mode, glassmorphism, and telemetry, not pastel colors.
- Required a functional Protocol Configurator to turn complex health goals into a 15-minute daily checklist.
- Demanded Market Proof: We needed to query the open web to prove the market was shifting from "Biohacking" to "Longevity Systems."
The 4-Day Build
We didn't just design a landing page; we built a functional React application and a Python-backed data engine.
Defining the "Operator's Lens"
- Discarded standard health tropes for a Cyber-Security / Spec-Ops aesthetic.
- Defined the "MQP Design System": Slate-950 backgrounds, Emerald-500 accents, and blurred glass cards.
- Mapped the User Journey: Mission (Manifesto) → Protocol (Configuration) → Telemetry (Proof) → Store (Execution).
Building the Protocol Configurator
- Built the Interactive Protocol View: Users select a goal (Cognitive vs. Physical) and the app generates a dynamic checklist.
- Implemented "GlassCard" and "Terminal" components to reinforce the high-tech feel.
- Created the Telemetry View using Recharts to visualize sleep data, HRV, and biological age reduction.
Python Data Pipeline & Social Signals
- Wrote a Python Colab script using Google Custom Search APIs to harvest 2,000+ social signals (Reddit, LinkedIn, X).
- Filtered data for keywords like "Blueprint," "HRV," and "Creatine" to analyze Share of Voice (SOV).
- Cleaned and structured the dataset to feed directly into the frontend dashboard.
Visualizing the Market & Launch
- Built the "MQ Intel" Dashboard: A React view that parses the CSV data to show "Topic Share" and "Volume Trends."
- Integrated Automated Insights: The system flags "Protocol Opportunities" (e.g., confusion around Zone 2 cardio).
- Deployed the "Store" view to monetize the protocol immediately via specific supplement stacks.
Net result: The client went from a concept to a live, data-backed platform that not only provides a health protocol but monitors the market conversation around it in real-time.
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