Pioneering 24/7 AI-Driven Well-Being Solutions

Business Problem

Mira aimed to develop a multi-agent AI system to provide personalized recommendations based on users’ daily habits and routines. Key challenges included:

Hyper-Personalized Recommendations: Real-time delivery of personalized suggestions tailored to individual user profiles.
Data Integration Gaps: Disparity between manual data entries (mood, diet) and automated inputs (wearables) led to inconsistent insights.
Predicting Life Disruptions: The platform struggled to simulate life disruptions (e.g., sleep loss) and predict their ripple effects on well-being.
Absence of a Feedback Loop: Without an efficient feedback loop, MIRA’s recommendations lacked continuous refinement.
Scalability: The system needed to process high volumes of data efficiently in real time.

About Mira

Mira is an innovative AI-driven platform that acts as a 24/7 co-pilot for personal well-being, offering users real-time insights and personalized guidance. MIRA’s multi-agent system helps users maintain balance and optimize their health by seamlessly managing disruptions to daily routines, fostering sustainable long-term well-being.

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Solution

Mira partnered with GoML to create an AI-driven platform designed to overcome these challenges through:

1.

Integrated Data Input Layer:
GoML synchronized manual inputs (mood, diet) and automated data from wearables (Fitbit, Apple Health) using Node.js and MongoDB, ensuring real-time access to up-to-date biometric information.

4.

Multi-Agent Framework:
Specialized AI agents, such as the Holistic MIRA AI Agent, were deployed with TensorFlow and PyTorch. These agents worked in collaboration with OpenAI to deliver real-time, hyper-personalized recommendations.

2.

Data Processing & Knowledge Management:
A middleware built using Node.js processed and structured incoming data into dynamic Knowledge Marts (e.g., Sleep, Stress) for easy retrieval and analysis using MongoDB and PostgreSQL.

5.

Feedback Loop Integration:
A Reinforcement Learning from Human Feedback (RLHF) system allowed continuous improvement of recommendations based on user interactions, with communication facilitated by Amazon SES.

3.

Simulation & Disruption Modeling:
GoML developed a simulation engine using Python, leveraging Bayesian inference to predict the effects of disruptions (e.g., stress) on well-being, running on AWS Lambda and EC2 for scalability.

Architecture

Data Preprocessing
– Data is processed through preprocessing engines to create business intelligence hierarchies and prepare it for further analysis.
Machine Learning Integration
– Models are trained using Amazon SageMaker and used for clustering, rebalancing, and generating insights.
Knowledge Graph Creation
– Data is organized into a Knowledge Graph using Amazon Neptune, enabling advanced query capabilities.
Panel of Expert AI Agents
– Multiple agents handle tasks like data validation, feedback integration, and scenario simulation for enhanced decision-making.
Simulation & Disruption Forecasting
– A simulation engine forecasts disruptions and performs scenario analysis, using both machine learning and rule-based decision-making.
Report Generation & Conversational Bot
– A FAQ bot interacts with the FAQ Hierarchy to generate reports and respond to user queries.
Data Storage
– Amazon S3 and Redshift are used for data storage and scheduled refreshes from the data sources.
Security & Governance
– Data is protected through encryption, logging, and access management, with strict controls over user roles and data flow.
Compliance Layer
– The system incorporates a governance layer for compliance reporting and ensuring security protocols are followed.

Outcomes

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Improved Accuracy in predicting user disruptions, thanks to Bayesian inference and the multi-agent framework.

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Increase in Engagement through real-time, hyper-personalized recommendations.

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Accuracy in reflecting user states through advanced data processing and simulation, positioning MIRA as a leading AI-driven well-being solution.

Technology Stack