How Ascentic used AI to modernize Brainsafe's legacy application

Customer overview

Brainsafe develops digital solutions for concussion management and brain health.

Its platform supports sports organisations throughout the complete concussion management journey, from baseline assessments and sideline evaluations to structured return-to-play rehabilitation. Coaches, medical staff and administrators rely on the platform to assess athletes, monitor recovery and collect critical health data.

Like many software companies with products that have evolved over several years, Brainsafe eventually reached a point where technical debt began limiting future development. Rather than continuing to patch the existing platform, the company decided to modernize its core technology and establish a foundation that could support future growth.

The challenge

Brainsafe's mobile application was built on an outdated cross-platform framework that had fallen behind current standards. When Google Play introduced new compliance requirements, it became clear that upgrading the existing application would require nearly as much effort as rebuilding it from scratch.

The challenge extended beyond outdated technology. Years of development had left the platform with incomplete documentation, evolving database structures and undocumented business logic. Understanding how the system actually worked had become one of the project's biggest challenges.

This is a situation many organisations recognise. Legacy applications often become business-critical while gradually turning into black boxes that few people fully understand.

Typical scenarios

This approach is particularly relevant for organizations with:

• Legacy applications built over many years
• Outdated or missing documentation
• Technical debt limiting future development
• Compliance or framework upgrade requirements
• Modernization initiatives with uncertain scope

Our approach

Before writing any new code, the team focused on understanding the existing system.

The team fed all available materials into Claude - existing source code, historical documentation, knowledge transfer recordings, database schemas, and user management modules - and used it to reconstruct a working understanding of the system's actual behaviour.

Where the AI surfaced conflicts between documentation and implementation, those were escalated to BrainSafe stakeholders for resolution. The output was a clean, validated requirements baseline before a single line of new code was written.

From there, the team adopted a spec-driven development approach using OpenSpec specifications. Every feature began with a written spec defining the requirement, the implementation plan, and the expected validations - agreed between the team and the AI before development started. 

This kept the work grounded and reduced the risk of the AI drifting from the agreed scope.

The architecture was designed with future scalability in mind - both technical and commercial. The team built with the expectation that BrainSafe would eventually need to expand into new markets and add new product lines, and left the structural flexibility to support that without requiring another major overhaul.

Implementation

Over a three-month period, the team delivered:

• A completely rebuilt React Native mobile application
• Two new React web applications
• Infrastructure-as-Code using Terraform
• Automated Azure and Keycloak configuration
• Full user and data migration without downtime
• Comprehensive and up-to-date technical documentation

AI supported the entire development lifecycle, including:

• Requirement discovery
• Architecture validation
• Code generation
• Documentation
• Infrastructure generation
• Data migration
• Automated code reviews
• Figma-to-code workflows
• Database analysis and debugging

Rather than replacing engineers, AI enabled the team to focus on architecture, validation, and engineering decisions while dramatically accelerating implementation.

Impact

The new platform was delivered before the Google Play compliance deadline with no interruption for existing users.

Brainsafe now has a modern platform capable of supporting:

• Faster feature development
• Improved scalability and reliability
• Improved user experience
• Better data consistency
• New customer segments
• Additional assessment types

The team estimates that a similar project using traditional development methods would likely have required between six to nine months. AI reduced months of requirement discovery into weeks while enabling a relatively small team to deliver an enterprise-grade modernization project.