Case study

Strengthening a digital respiratory platform through technology risk diagnosis

  • Digital health, respiratory care
  • Technology risk diagnosis
  • Taking responsibility
  • Scalability and system reliability
  • Team structure assessment
  • Architecture review
  • Risk mitigation roadmap
  • Codebase audit
LLI Role
Technology Risk Diagnosis
Summary
We partnered with AioCare to conduct an in-depth due diligence analysis of their portable respiratory monitoring and treatment system for asthma and COPD. Our objective was to assess the stability, scalability, and maintainability of their existing codebase and evaluate the effectiveness of their technology team structure.

The problem

AioCare operates in a highly sensitive domain where medical accuracy, data integrity, and system reliability are critical. The platform combines hardware, mobile applications, and analytics, which increases architectural complexity. Before scaling further, the company needed clarity on:

  • Overall code quality and technical debt
  • Architectural scalability and long-term maintainability
  • Security and data handling risks
  • Team structure, effectiveness, and delivery capacity
  • Dependency risks related to specific developers or components

In digital health, technical instability or poor architectural decisions can directly impact patient trust, regulatory exposure, and investor confidence. The leadership team required an objective, external assessment before making further strategic investments.

Why LLI

In a high-risk, regulated environment like digital health, leadership needs an objective view and clear translation of technical complexity into business risk.

LLI fits this context by combining deep technical expertise with a structured, decision-oriented approach, helping leaders focus on what actually matters and make confident decisions around scaling and investment.

Our approach

We applied our Technology Risk Diagnosis framework to deliver a structured, business-oriented evaluation.

  1. 01

    Evaluation of development workflows and quality assurance processes

  2. 02

    Static and structural analysis of the codebase

  3. 03

    Review of system architecture and integration points

  4. 04

    Assessment of team roles, seniority balance, and knowledge distribution

  5. 05

    Identification of operational and delivery bottlenecks

We translated technical findings into business risk categories, ensuring that management could clearly understand the impact and urgency.

Scope covered

Our analysis covered the full technology landscape behind the platform, not just selected components. We reviewed mobile applications, backend services, analytics layers, cloud infrastructure, CI CD pipelines, and QA processes as one interconnected system. This allowed us to identify not only isolated issues, but also dependencies and risks that emerge across components and teams.

The solution

We delivered a comprehensive due diligence report, including:

  1. 01

    Clear mapping of critical, high, and moderate technical risks

  2. 02

    Identification of architectural constraints limiting scalability

  3. 03

    Analysis of potential single points of failure

  4. 04

    Assessment of documentation and knowledge sharing gaps

  5. 05

    Recommendations for restructuring technical leadership and ownership

  6. 06

    A prioritised roadmap for stabilisation and improvement

  7. 07

    Where appropriate, we also outlined options for interim technical leadership support to accelerate stabilisation and enforce best practices.

Technology stack

The platform's technology choices were evaluated as part of the diagnosis, assessed for stability, security, and long-term durability.

  • Backend

    Ruby on Rails

    Mature ecosystem and proven security practices, a stable foundation for sensitive data handling.

  • Frontend

    Vue.js

    Fast, accessible, and responsive interfaces built for users across varying devices and connection speeds.

  • Infrastructure

    AWS

    Scalable and reliable performance across Europe, with the resilience required for regulated deployments.

  • Testing

    Robot Framework + Python

    Automated and repeatable quality assurance ensuring consistent behavior across every release.

These were not trend-driven choices. They were durability-driven decisions.

Engineering standards

To build a trustworthy platform, tools alone aren't enough. That's why we emphasised our engineering approach:

  • Clean, readable, maintainable code

    Written for the next developer, not just the current sprint.

  • Consistent development and review practices

    Standardised workflows across the full delivery lifecycle.

  • Transparent documentation for future contributors

    Every module and decision documented for long-term handover.

  • Close collaboration between developers, QA, and project leadership

    Shared acceptance criteria and no surprises at delivery.

Public-good platforms deserve the same engineering standards as enterprise systems.

Results and impact

For Users

  • Clear visibility into real technical and organisational risks
  • Structured decision-making framework for future investment
  • Reduced uncertainty before scaling or fundraising
  • Independent validation of strengths and weaknesses

For the Product and Team

  • Actionable improvement roadmap
  • Better alignment between architecture and product vision
  • Reduced dependency risk
  • Foundation for improved quality standards and scalability

Why this case matters

This case shows where digital health companies actually lose control, inside technology, not at the product level.

AioCare didn't need more features. They needed clarity on whether their system could scale safely.

This is where LLI brings value:

  • We expose hidden risks.

  • We translate tech into business impact.

  • We give leadership a clear basis for decisions.

  • Before you scale, you need control.

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