Baby breathing monitor system upgrade: 25% reduction in development costs

Innowise has significantly upgraded a baby breathing monitoring platform and prepared the system for feature set expansions and scaling.

Customer

Industry

Healthcare

Region

Client since

2022

The customer is a global leader in high-tech and consumer electronics, with over 50,000 employees across 9 facilities worldwide. They manufacture a wide range of products, including home appliances, broadcasting equipment, personal computers, mobile devices, and components for the telecommunications and data communications industries.

The company has a specialized division dedicated to baby healthcare devices. This division focuses on creating innovative products, such as breathing monitors, video baby monitors, and dreamers, designed to monitor infants’ breathing patterns and improve their sleep. Parents can access essential health monitoring and support through web and mobile applications.

Detailed information about the client cannot be disclosed under the provisions of the NDA.

Challenge

Overcoming early-stage application instability and poor code structure

The customer had mobile and web applications for their baby breathing monitor system, but these were in their early stages and plagued by numerous bugs. The poorly structured codebase hindered both bug fixes and the development of new features. System delays and instability led to increased support and development costs, negatively impacting financial performance. Additionally, these issues diminished the company’s competitiveness and market share.

Innowise was engaged to stabilize the environment, resolve existing bugs, and prepare the system for future feature additions and scaling.

Solution

Implementing comprehensive bug fixing, system refactoring, and automated deployment to improve efficiency

Innowise addressed the challenges by fixing bugs on both the backend and frontend. We restructured the codebase, created new microservices to improve modularity, and migrated the database from PostgreSQL to AWS DynamoDB to improve scalability and reduce costs. Our team also implemented CI/CD pipelines to automate deployment and ensure code quality. Additionally, we were responsible for automating deployment and infrastructure versioning using Terraform.

Bug fixing and testing

First, we addressed critical bugs impacting system performance and reliability. Simultaneously, our team developed comprehensive unit and integration tests to verify the functionality of both new and existing features. This rigorous testing allowed us to identify and address potential issues early on, ensuring a high-quality end product.

Code refactoring

The existing codebase was unstructured and didn’t follow PEP-8 standards and clean architecture principles, making it difficult to work with. Our team systematically refactored the code, aligning it with PEP-8 guidelines and improving its overall structure. This process involved cleaning up the code, optimizing functions, ensuring consistency, and adhering to SOLID principles.

Documentation

Our team created detailed technical documentation for the existing codebase, new features, microservices, and deployment processes. This comprehensive documentation enabled current and future developers to understand the system better and contributed to smoother onboarding and knowledge transfer.

Development of microservices

In addition to stabilizing the existing baby breathing monitor system, we developed new microservices utilizing a containerization platform like Docker and orchestrated them using Kubernetes. This enhanced modularity and scalability, allowing for independent deployment and scaling of individual services.

Each microservice, such as the breathing pattern analysis module or the alarm notification service, was designed to handle specific tasks, communicating with each other through lightweight RESTful API protocols. This architecture improved the system’s overall efficiency, performance, and fault tolerance, as individual services could be updated or replaced without affecting the entire system.

Database migration

Our experts managed the migration from PostgreSQL to AWS DynamoDB to leverage its scalability, performance, cost-effectiveness, and flexible data modeling capabilities. This transition involved a meticulous process of data schema redesign to align with DynamoDB’s non-relational structure, careful data mapping and transformation, and thorough validation to ensure data integrity.

Additionally, we optimized query patterns and indexing strategies to maximize DynamoDB’s performance capabilities for the specific access patterns of the baby breathing monitor system.

CI/CD implementation

We implemented continuous integration and continuous deployment (CI/CD) pipelines using Jenkins and GitLab CI to automate the build, test, and deployment processes. This streamlined development workflows and ensured rapid feedback loops. Additionally, we employed Terraform to define and manage our cloud infrastructure as code. This enabled us to provision and scale resources efficiently, maintain infrastructure consistency across environments, and track changes through version control.

We also integrated automated testing frameworks into our CI/CD pipelines to ensure code quality and application stability before deployment. This seamless combination of CI/CD practices and IaC significantly reduced manual intervention, minimized errors, and accelerated release cycles, resulting in a more robust and reliable baby breathing monitor system.

Technologies

Frontend

JavaScript (React, TypeScript), Redux

Backend

Python (FastAPI), Java, Kotlin

Cloud

AWS, Lambda, SQS, SNS, SES, IoT Core, Timestream, Cognito, DynamoDB

VCS

Git, GitLab

Tools

Material-UI, FastAPI, Tortoise ORM, boto3

Process

We started with a discovery phase, conducting initial meetings with the company’s stakeholders to understand their pain points and requirements thoroughly. Our business analysts then created a comprehensive document detailing the key improvements and ensuring the proposed solution’s technical viability.

With the client’s requirements clearly documented, the Innowise development team began the software engineering process. Using the Scrum methodology, we organized our work into two-week sprints to maintain regular progress and adaptability. We held triweekly meetings to address critical issues and ensure effective coordination. Additionally, the project manager conducted weekly calls to update the client on development progress and gather feedback, allowing us to continually refine our approach.

Team

Project Manager

Business Analyst

Full-Stack Developers

Manual QA Engineer

QA Automation Engineer

Results

Enhanced user experience and market position with optimized and scalable system

The implementation of the project led to a significant improvement in system stability and reliability, resulting in increased user satisfaction and enhanced brand trust. The optimized architecture and rewritten codebase simplified further development and support, reducing development costs by 25%.

Migrating to a higher-performing database and introducing microservices provided flexibility and scalability, enabling a faster response to user growth. Automating testing and deployment also cuts down the time it takes to release updates, speeding up the launch of new features.

As a result, the customer strengthened their position in the infant care device market with an improved solution, achieving a 15% increase in market share.

Project duration

June 2022 - September 2023

25%

reduction in development costs

15%

increase in market share

Related cases

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Baby breathing monitor system upgrade: 25% reduction in development costs

Customer

Challenge

Overcoming early-stage application instability and poor code structure

Solution