AI PRODUCT · DIGITAL WELLNESS · CONNECTED INTELLIGENCE
An adaptive AI-powered wellness platform connecting nutrition, body composition, workouts, smart-device data, meal planning, groceries, and personalized coaching into one continuously evolving experience.
Professional project profile
This profile presents Antonio Sabarez II’s publicly documented responsibility areas. Detail remains intentionally limited until it is approved for public release.
Selected product experience
Executive summary
PAi Coach was conceived as an integrated consumer wellness platform that connects what a person plans to do with what they actually eat, how they train, and how their body changes over time. It brings together AI-assisted nutrition, personalized meal planning, recipes, grocery planning, workout intelligence, body-composition tracking, connected-device measurements, progress analytics, and conversational coaching. A feedback-loop architecture connects goals and preferences with changing measurements, actionable plans, and subsequent adaptation.
Consumers can use separate tools for food, workouts, body composition, recipes, and coaching. PAi was designed around the integration problem: turning user context and changing real-world measurements into coordinated nutrition, shopping, training, and coaching experiences rather than asking people to reconcile disconnected information manually.
Create a connected product experience in which real-world inputs can influence future plans while product, business, and technical decisions remain aligned.
Executive responsibility framework
Defined the product vision and broader business direction for PAi, including market positioning, commercialization strategy, product evolution, subscription-based opportunities, ecosystem thinking, and the path from an application concept toward a scalable AI-enabled wellness platform.
Evaluated monetization, subscription strategy, pricing considerations, operating economics, commercialization opportunities, growth requirements, and investment and scaling considerations as part of product planning and business decision-making.
Led product definition, requirements, prioritization, user workflows, and roadmap development across AI coaching, nutrition, recipes, meal planning, groceries, Body Intelligence, Workout Intelligence, connected devices, community experiences, subscriptions, and ongoing UI/UX modernization.
Designed and governed the solution architecture spanning mobile application, backend services, database, REST APIs, AI and LLM integration, BLE-connected devices, cloud infrastructure, and deployment, while defining boundaries between deterministic business logic and generative intelligence.
Performed and directed hands-on full-stack engineering across Flutter and Dart, FastAPI and Python, PostgreSQL, APIs, business logic, AI integration, BLE devices, testing, debugging, release engineering, cloud deployment, and ongoing production-oriented development.
The solution
Adaptive intelligence loop
PAi is designed around continuous adaptation: changing inputs can influence future plans rather than leaving recommendations permanently fixed.
Goals · preferences · schedule · training
Measured food · body composition · activity · progress
Deterministic logic + AI/LLM intelligence
Meals · recipes · groceries · workouts · coaching
New signals feed the cycle again
Precision nutrition
PAi’s nutrition experience connects planning with what a user actually consumes. Personalized meal planning and recipes work with BLE food-scale measurement so food quantity can become a measurable input rather than relying exclusively on manual estimates. AI Vision is a secondary flexibility and adherence mechanism when precision measurement is impractical, such as meals away from home.
Body intelligence
Body Intelligence extends beyond simple weight tracking by connecting body-composition measurements, progress trends, goals, reliability considerations, achievements, wellness context, adaptive interpretation, and education around important metrics. It is presented for consumer wellness and progress understanding—not medical diagnosis or clinical monitoring.
Workout intelligence
Workout generation and planning, exercise and anatomy information, training methodology, recovery considerations, workout execution and tracking, WOD experiences, and coaching are architected as connected product domains. Nutrition, body-composition information, training, progress, and coaching are not treated as unrelated features.
Planning to action
Recipes and meal plans can translate into grocery requirements, bridging the gap between deciding what to eat and obtaining what is needed to prepare it. Longer-term direction includes ingredient normalization, external shopping destinations, cost awareness, and optimization without representing PAi as a grocery-delivery company.
Technology & architecture
Flutter · Dart
FastAPI · Python · REST APIs
PostgreSQL
OpenAI / LLM integration · Adaptive intelligence · Deterministic intelligence
Bluetooth Low Energy · Smart food scale · Body-composition scale
Linux · NGINX · HTTPS/TLS · Cloud/VPS deployment
The architecture allows PAi to continue expanding across adaptive wellness intelligence, connected devices, richer coaching, community experiences, commerce integrations, and increasingly personalized planning while maintaining clear boundaries between deterministic application logic and AI-generated experiences.
Product scope
PAi is designed for consumer wellness, fitness, nutrition, and lifestyle support. It is not presented as a medical diagnostic, treatment, or clinical decision-making system.
Public case study
Public case-study content intentionally excludes proprietary source code, private data, credentials, internal prompts, confidential algorithms, and protected implementation details.
Project evidence
Live application
View Live ProductPublic evidence and status
Public status: MVP v1.0 Released. A public product website and selected visual evidence are available below.
Current boundary
Public case-study content intentionally excludes proprietary source code, private data, credentials, internal prompts, confidential algorithms, and protected implementation details.
Market intelligence and planning
PCMIS is a separate market-intelligence, scenario-modeling, and financial-planning system that supports strategic planning for PAi while standing independently as a decision-support product case study.
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I work across business analysis, financial planning, product strategy, solution architecture, and hands-on engineering to move complex ideas from concept through implementation and deployment.
