AiGenics
Empowering Mental Wellness Through Scalable Data Engineering: The AiGenics Moodology App
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2021 - Galshow, UK
Embedded Software Products
10-50 employees
Overview
AiGenics is at the forefront of integrating Artificial Intelligence (AI) into healthcare, developing both in-house solutions and collaborating with healthcare providers to enhance their platforms using AI technologies, including Machine Learning and Natural Language Processing. Their flagship product, the Moodology App, delivers personalized mental health support through digital Cognitive Behavioral Therapy (CBT) and self-meditation tools. As AiGenics sought to scale the platform and improve its data processing capabilities to meet growing user demand, they partnered with Folio3 to optimize the app’s infrastructure, ensuring seamless integration of mental health resources, real-time insights, and secure data handling for enhanced user interactions and outcomes.
The Challenge – Data Engineering for Scalable Mental Healthcare
AiGenics faced several data-related challenges, including:
Handling Large Data Volumes: Efficiently managing and storing data from thousands of therapy sessions and user interactions.
Scalable Data Processing: Maintaining high performance as user traffic grew.
Data Security and Integrity: Ensuring user privacy and data accuracy in compliance with health regulations.
Real-Time Insights: Providing timely, data-driven insights to enhance mental health interventions.
The Solution – Advanced Data Engineering for Mental Wellness
To meet these challenges, Folio3 deployed an advanced data engineering solution that redefined AiGenics' data infrastructure:
Data Scalability and
Elasticity
Leveraging AWS S3 for secure, scalable storage, enabling the platform to manage vast datasets of therapy sessions and user interactions.
Scalable EC2
Infrastructure
Utilizing AWS EC2 instances to support the app’s ability to auto-scale and manage high-traffic periods during therapy sessions and content streaming.
Efficient Data
Pipelines
Implementing data pipelines using SQL Server for optimal user data management, while employing parallel ingestion techniques to maintain real-time processing.
Data Integrity &
Security
Ensuring secure processing and maintaining data accuracy through Serilog and Hangfire, allowing transaction logs to be traceable and verifiable.
Real-Time Data
Management
Optimizing data ingestion and retrieval processes to enable real-time mental health insights for clinicians and users, driving faster interventions.
Technologies Involved In This Case
Development Stack
ASP.net
React Native
SQL Server
Hangfire
Serilog
Infrastructure Stack
EC2 instances
Amazon S3
Results & Achievements
500,000+ User Sessions per Day
The system’s scalable architecture now supports over 500,000 user sessions daily, ensuring seamless experiences for users and clinicians alike.
40% Faster Data Processing
New pipelines reduced processing times significantly, accelerating the delivery of mental health insights.
Real-Time Insights
The platform now offers real-time insights, enabling clinicians to make data-informed decisions more efficiently.
Cost Savings
By optimizing data workflows, the infrastructure reduced operational costs by over 50%, freeing resources for further development.
Swift Time-to-Market
The project was completed in just four months, rapidly delivering a robust solution to the market.