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.
Practice Intuition
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
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.
The product is documented as a PRD-to-MVP translation: a structured assessment flow, protocol engine, safety boundary model, and future telemetry loop.
Build a polished entry point with assessment, pillar resources, and protocol output.
Live build linked from hero.
Use structured LLM output so the assistant returns renderable product data instead of raw chat.
Checklist-style protocol workflow.
Next layer: saved progress, protocol quality review, telemetry, and follow-up loops.
Roadmap-ready product surface.
Classification, clarifying questions, JSON-style responses, and UI-rendered protocols.
Medical boundary setting, escalation language, and wellness-coaching constraints.
React, TypeScript, branded interaction design, and PRD-to-product implementation.
Users describe a current wellness challenge in natural language, optionally adding daily check-in context before protocol generation.
The assistant classifies the challenge across TIME, SPACE, SELF, or multi-pillar patterns so the response has a clear conceptual home.
The model returns structured coaching data that can render as checklist-style protocol cards rather than loose chat advice.
Bonita was the proof-of-concept layer for a broader Somanaut direction: a coaching system where wellness intelligence is structured, safety-aware, and repeatable enough to become a product rather than a conversation demo.
Bonita Encode is a consumer-facing MVP for integrated wellness guidance. It helps users name a current problem, classify the issue across TIME, SPACE, and SELF, and receive one protocol that is specific enough to try immediately.
Many wellness tools are too narrow, too generic, or too dependent on passive tracking. Users need personalized guidance that connects body, mind, timing, and environment while teaching the reasoning behind each recommendation.
Future versions should move model calls behind a secure backend, add persistence, expand safety coverage, support citations or evidence provenance, and turn mock longitudinal concepts into real progress tracking.
Bonita can evolve into a premium AI wellness intelligence platform that combines personalized behavior-change support, integrative health education, and longitudinal self-awareness while staying clear about medical boundaries.