Allez Health is a digital product for continuous glucose monitoring. It combines iOS and Android mobile apps, Bluetooth sensor integration, algorithmic measurement processing, personalized notifications, reports, and a scalable server platform.
We developed the full technical foundation of the product: native mobile apps, backend, API, microservices, cloud infrastructure in AWS, tests, documentation, and deployment processes. The main focus was stability, data accuracy, and reliable operation in daily medical monitoring scenarios.
Challenge
In health monitoring products, simply showing data on a screen is not enough. The app must connect to the sensor reliably, receive measurements regularly, process them correctly with an algorithm, and notify the user about critical status changes in time.
A separate challenge was the server platform. We had to design an API and infrastructure capable of working with accounts, devices, a large stream of metrics, reports, tests, documentation, and scaling under high load.
Our role
We were responsible for developing native iOS and Android apps, backend, API, cloud infrastructure, DevOps, documentation, and testing. The work covered both mobile app user scenarios and server-side architecture for storing, processing, and synchronizing data.
The product architecture was built around several critical areas: mobile client, sensor connection, algorithmic measurement processing, API, microservices for accounts and metrics, cloud environment, automated tests, and contract documentation.
Sensor and mobile app
The key scenario starts with sensor connection. The app includes device search, Bluetooth connection, user onboarding, and informational screens that help configure monitoring correctly.
After connection, the app regularly receives data from the sensor, passes it to the internal glucose calculation algorithm, and shows the user the current status, trend, and important changes in a clear form.
Monitoring and context
The main screen is built around daily control. The user sees the current value, recent status, and nearest trend to quickly understand what is happening right now.
The trend chart shows data for 12 hours, 24 hours, and 3 days. The user can add events such as meals, physical exercise, and other activities. This helps analyze measurements not in isolation, but together with the context in which they appeared.
Analytics, reports and notifications
The app includes statistics for 24 hours, 7 days, and 30 days with comparison to previous periods. Report generation for the last 15 days was also implemented, with summary data, charts, and statistics for analyzing the condition and discussing results with a specialist.
Notifications are configured according to the user’s individual limits. The app alerts users when glucose rises above or falls below the configured range, and also allows changing notification levels and schedule.
Backend and data
The server side consists of an API and a set of microservices for users, accounts, devices, and collected metrics. The backend is responsible for data storage, synchronization with mobile apps, report preparation, and stable operation of service contracts.
API documentation was prepared according to standards so the mobile, backend, and QA teams could work with service contracts in sync. The backend also has 100% unit test coverage.
Infrastructure and scaling
The cloud infrastructure was deployed in Amazon Web Services. The environment was designed with autoscaling, backups, stable operation under load, and industry-standard security and reliability practices in mind.
The infrastructure is described with Terraform, so environments can be reproduced, controlled, and maintained for development, testing, and production. Performance and high-load tests showed that the platform in its base configuration can handle 10,000 requests per second, which is estimated to cover the needs of 1 million concurrent users.
Quality
The mobile apps are fully covered by unit tests and end-to-end tests. The interface works in light and dark themes, supports localization into multiple languages, and passes checks for key user scenarios.
Special attention was paid to the stability of scenarios that affect user trust: sensor connection, regular measurement intake, data processing, notifications, and report generation.
Result
Allez Health received a complete technical platform for continuous glucose monitoring: native mobile apps, sensor integration, algorithmic data processing, personalized notifications, charts, statistics, reports, backend, API, and scalable AWS infrastructure.
For the user, this means clear daily monitoring: current values, trends, event context, reports, and timely alerts about critical status changes. For the product, it means a stable technical foundation ready for scaling and further development.
