Reimagining Human Wellness with Inference-Based AI Systems: CSO

CSO is an AI-powered holistic health platform that helps users understand and improve their overall wellbeing through guided conversations. By combining conversational AI and emotional intelligence, CSO delivers personalized wellness advice, enabling users to track progress and make informed lifestyle changes over time.

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OVERVIEW

Project Overview

Mental health and wellness apps often focus on isolated activities like meditation, sleep, or fitness, leaving users without a holistic understanding of their internal health state. Most of the platforms fail to explain how different aspects of wellbeing influence each other.

Our objective was to build a mobile-first AI platform that diagnoses wellness at a deeper level. The solution needed to interpret subjective inputs such as conversations and emotions, convert them into structured intelligence, and continuously adapt as users’ behaviors and mental states changed.

We developed CSO, a wellness platform powered by continuous, passive assessment and inference AI wellness platform centered around a Core Systems Intelligence Diagnostic (CSID). Through guided text and voice conversations with an AI assistant named Mandi, CSO maps user responses using ladder logic, connecting upstream signals and downstream patterns across the conversation to evaluate individuals across twelve core health systems. It generates real-time scores, tracks progress over time and assigns adaptive wellness tasks based on evolving inputs. CSO does not diagnose conditions or provide medical advice. It functions as a non-clinical conversational AI that listens, observes, and continuously maps user patterns in the background.

Developed using React Native, Node.js, MongoDB, and ExpressJS, CSO delivers a privacy-first architecture capable of handling long-term conversational data, and emotional analysis. The platform bridges the gap between generic wellness apps and clinical tools, empowering users with clarity without replacing professional care.

Key Features

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01.

Conversational AI Health Assessment with Mandi

CSO enables users to interact with an AI assistant named Mandi through text or voice conversations. Mandi conducts adaptive conversations using ladder-based logic to understand the user’s mental and emotional context. Each response influences the next question, allowing deeper insight without overwhelming the user. This approach makes health assessment feel natural while keeping diagnostics consistent behind the scenes.

02.

CSID Scoring Across Core Systems and Sub-Systems

Core Systems Intelligence Diagnostic (CSID), is a scoring framework that evaluates users across 12 core health systems and 79 sub-systems. Each system is scored on a 0–10 scale, providing detailed visibility into wellbeing. Scores are calculated in real time based on conversational inputs, emotional signals, and behavioral patterns, offering a clear understanding of the user’s current health state.

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03.

Dynamic, Day-to-Day Health Intelligence Tracking

As users continue to engage with Mandi, sub-system scores evolve based on daily conversations and lifestyle changes. Improvements in physical activity, sleep routines, or stress management reflect positively, while prolonged fatigue, emotional strain, or unhealthy habits result in score declines. This continuous recalibration ensures users see meaningful trends instead of one-time evaluations.

04.

Personalized Tasks, Interventions, and Follow-Ups

Based on CSID insights, CSO assigns personalized wellness tasks tailored to the user’s current challenges. These may include breathing exercises, yoga routines, home workouts, sleep schedule adjustments, dietary improvements, or habit-building activities. Mandi actively follows up on these tasks during subsequent conversations, tracking adherence, addressing obstacles, and refining recommendations to support long-term wellbeing.

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05.

Emotional Intelligence and Voice-Based Sentiment Analysis

CSO integrates emotional intelligence to enrich its assessments and guidance. Emotional tone is analyzed through both voice and text inputs, allowing Mandi to detect stress, anxiety, fatigue, or emotional shifts that may not be explicitly stated. These emotional signals influence follow-up questions, task assignments, and system scoring, making interactions more empathetic and context aware.

06.

Long-Term Wellness Memory and Supplement Planning

To support long-term engagement, CSO uses a dual-memory architecture that separates short-term conversational context from long-term summarized insights. After approximately one month of consistent interaction, CSO suggests a supplement plan based on accumulated data and sub-system trends. These recommendations are advisory and grounded in long-term patterns rather than isolated conversations.

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Features at a Glance

  • trumanConversational AI Health Assistant

    trumanText and Voice-Based Interactions

    trumanCSID Scoring System (0–10 Scale)

    truman12 Core Health Systems

    truman79 Wellness Sub-Systems

    trumanDynamic Daily Health Tracking

    trumanEmotional Intelligence and Sentiment Analysis

    trumanPersonalized Wellness Tasks and Follow-Ups

    trumanSupplement Recommendation Logic

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Project Challenges and Their Solutions

1. Managing Long-Term Conversational Context and Token Limits

Challenges

Extended text and voice conversations spanning weeks or months risked exceeding API token limits and increasing storage costs, while still requiring contextual continuity for accurate assessments.

SOLUTIONS

We implemented a dual memory architecture that stores detailed conversations in short-term memory for active context and scoring. After approximately 21–30 days, conversations are summarized into concise insights and stored in long-term memory, preserving continuity while optimizing performance and cost.

2. Emotional Analysis and Multi-API Coordination

Challenges

Integrating emotional intelligence, speech recognition, and conversational AI introduced complexity across multiple APIs, including latency, synchronization, and reliability concerns during voice-to-voice interactions.

SOLUTIONS

Following iterative testing and patching, we optimized API orchestration, introduced request throttling and retry logic, and stabilized voice-to-voice communication. Emotional analysis was integrated in a way that enhanced insight without disrupting conversational flow or user experience.

Tech Stack

React Native (Expo)

Used to build a cross-platform mobile application with a consistent, responsive user experience across devices.

Node.js & ExpressJS

Power scalable backend services for conversation handling, scoring logic, memory management, and API orchestration.

MongoDB

Stores structured user data, profiles, system scores, and interaction history efficiently.

Vector Database

Enables semantic retrieval of conversational context and long-term memory summaries.

OpenAI GPT APIs

Drive natural language understanding, guided dialogue, and insight generation.

Whisper

Supports speech recognition functionality for voice-based user interactions.

Docker & Firebase

Ensure scalable deployment, consistent environments, and reliable push notifications.

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