Beet.Health · AI Nutrition Platform
Turning a health-tech vision into a fundable MVP in eight weeks
An AI-powered platform connecting nutritionists and clients — helping nutritionists manage and guide clients at scale, while clients track their health through natural, conversational logging.
ROLE
Lead Product Designer (UX/UI)
TIMELINE
8 Weeks
PLATFORM
Web and mobile
SCOPE
Research to Dev Handoff
The Brief
An ambitious vision,
a lean runway
Beet.Health came to me with a brief for an AI-powered nutrition tracking platform, benchmarked against players like HealthifyMe, Sugar.fit, and TwinHealth — but built to give nutritionists a first-class role, not an afterthought. With an eight-week runway to a fundable MVP, I led the design end to end: from early scoping conversations through dev-ready handoff, across a nutritionist web platform and an AI-assisted client app. The product is now live at beet.health, being used to demo for funding.
The Research
Where the market was already crowded
Before any screens, I spent time understanding how existing players actually worked — combining desk research with hands-on testing of their apps. Across the board, one gap stood out: every competitor treated the nutritionist as an afterthought, not a primary user. Beet.Health's opportunity wasn't a better consumer app — it was building the first platform designed around the practitioner, with the client experience following from that.
Gap
Healthify Me
Sugar.fit
TwinHealth
Focus
Consumer self-tracking
Clinical / diabetes coaching
Device-integrated coaching
Practitioner role
Minimal — mostly consumer-facing
Structured, but rigid plans
High-touch, not self-serve
Client experience
Manual logging, generic AI tips
Clear plans, little flexibility
Passive — driven by wearables, not habit-building
Gap for nutritionists
No real collaboration layer
Limited flexibility to scale
Not built for scale
The Scope
Scoping down without losing the vision
The original brief was ambitious — a full practice-management platform: multi-clinic and multi-branch support with role-based permissions, a CRM-style dashboard tracking clinic KPIs and revenue, automated billing, and a phase-two roadmap covering biomarker insights, habit-building frameworks, and social accountability features. With an eight-week runway to a fundable MVP, the priority wasn't building the whole platform — it was proving the core loop. Working closely with the client, I helped narrow the vision down to what actually needed to exist to demonstrate real value.
Shipped (MVP)
Deferred to later phases
Nutritionist dashboard & client profiles
Multi-clinic / super-admin system
AI-assisted meal logging (photo + voice)
Automated billing & invoicing
Diet plan builder (with allergy safeguards)
CRM-style KPI dashboard
Daily health overview & progress tracking
Biomarker insights & longevity scoring
Ingredient checklist for grocery prep
Gamification & social accountability
Proving the core loop — a nutritionist actively guiding a client through AI-assisted tracking — mattered more than breadth for a platform trying to earn its next round of funding.
The Problem
Two users, two very different problems
Nutritionists and clients needed fundamentally different things from the same product — and most tools on the market solved for only one side.
Nutritionists struggle with
Clients struggle with
Tracking multiple clients via spreadsheets
Manual meal logging takes too much effort
Monitoring diet compliance at scale
Inconsistent hydration/supplement tracking
Identifying clients who need attention
Hard to understand daily health progress
Managing payments manually
Hard to stay consistent with tracking
Communicating through scattered channels
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The Approach
Less logging, more conversation
Traditional health apps treat AI as a bolt-on feature — a chatbot layered over a form-based flow. Beet.Health is built AI-first: from the moment you open it, the interface invites a conversation, not a form. Say what you ate, and it's captured, confirmed, and logged — no menus, no manual entry.
The Approach
One system, two experiences
Beet.Health connects nutritionists and clients through two synchronized platforms — plans and feedback flow one direction, real-time data and AI-processed insights flow back the other.
The Process
Eight weeks, start to finish
With the MVP scope locked, the work moved through five focused phases — from early research to a dev-ready handoff — designed to keep both platforms moving in parallel rather than sequentially, so nothing sat waiting on the other.
The Solution
Making complex data scannable
Nutritionists manage dozens of clients at once — raw tables and numbers make it nearly impossible to spot who needs attention. Instead of a data dump, the dashboard leads with visual indicators: meal compliance, weight trend, and client status at a glance, so a nutritionist can act in seconds, not minutes.
The Detail
Building plans without creating conflicts
Diet plans needed to be both fast to create and safe to assign. When a nutritionist tries to assign a plan containing an ingredient a client is allergic to, the system flags it immediately — and can generate an alternative version with safe substitutions on the spot, rather than leaving the nutritionist to manually rework the plan.
The Outcome
From brief to fundable product
Beet.Health is live at beet.health, using these designs as the foundation for its practitioner and client experience — currently being used to demo the product for funding conversations. The MVP scope — two connected apps plus a marketing site — went from a single client brief to dev handoff in eight weeks.
The Takeaway
What this project reinforced
Designing for two very different users meant constantly prioritizing whose problem a given screen was actually solving. Scoping under a hard deadline meant learning to separate what proves the concept from what completes the vision. And building an AI-first interaction model made clear how much friction disappears when the interface trusts the user to just describe their day, instead of asking them to fill out a form about it.
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