Codes by Shrey

Practice Intuition

Bonita Encode

An AI wellness intelligence MVP that helps users route real-world challenges into TIME, SPACE, and SELF, then receive one structured protocol with clear steps, rationale, and safety boundaries.

Product System

  • TIME: circadian rhythm, sleep, timing, energy
  • SPACE: body, movement, pain, fascia, environment
  • SELF: mood, cognition, emotional regulation, identity
  • IQ / EQ / KQ wellness dashboard framing

Domain

AI wellness intelligence

Stack

React, TypeScript, Gemini

Pattern

Assessment to protocol

Safety

Medical boundaries and escalation

Problem

Most wellness products isolate sleep, exercise, mood, or tracking. Bonita treats wellness as an interconnected system and gives users one actionable next step instead of an overwhelming plan.

Experience

Users can start from a branded entry point, browse pillar resources, complete a chat-based assessment, add daily check-in context, and generate a broader wellness dashboard.

AI Behavior

The assistant classifies user needs, asks focused clarifying questions, returns structured JSON, renders checklist-style protocols, and preserves clear boundaries around medical advice.

Product Value

The MVP demonstrates PRD-to-product translation: a premium-feeling front end, structured LLM output, safety checks, local telemetry concepts, and an extensible protocol framework.

Process + Progress

The product is documented as a PRD-to-MVP translation: a structured assessment flow, protocol engine, safety boundary model, and future telemetry loop.

PRD

Define the TIME, SPACE, SELF pillars and the wellness intelligence framing.

PI Bonita PRD v1

MVP

Build a polished entry point with assessment, pillar resources, and protocol output.

Live build linked from hero.

Protocol

Use structured LLM output so the assistant returns renderable product data instead of raw chat.

Checklist-style protocol workflow.

Iteration

Next layer: saved progress, protocol quality review, telemetry, and follow-up loops.

Roadmap-ready product surface.

Technical Skills Demonstrated

Structured LLM Output

Classification, clarifying questions, JSON-style responses, and UI-rendered protocols.

Product Safety

Medical boundary setting, escalation language, and wellness-coaching constraints.

Frontend Delivery

React, TypeScript, branded interaction design, and PRD-to-product implementation.