Revealix

Revealix is a digital health company dedicated to simplifying foot health and making preventive care more accessible through proactive, personalized and technology-enabled solutions. The company provides a tech-driven approach for the early detection and management of foot health issues, primarily focusing on preventing complications related to diabetes. Revealix assists clinical teams in streamlining and scaling their amputation prevention practices by offering real-time monitoring of skin temperature changes, customized risk scores and educational resources.

Service: Machine Learning & AI, Software Quality Engineering, UI/UX Design & UI Development, DevSecOps

Industry: Healthtech/Biotechnology

Tech Stack: PostgreSQL and Selenium, Python, Swift

Profile & Challenge

As an Austin-based startup founded in 2014, Revealix aims to make proactive foot health management more accessible by combining AI-driven assessments with intuitive, technology-enabled tools. Their platform captures thermal imaging data, which is processed through machine learning models to identify patterns associated with diabetes-related complications, such as ulcers and amputations. While the AI model does not provide a medical diagnosis, it generates valuable clinical observations that empower healthcare providers to take early, preventive action.

Revealix needed to transform their AI model from a standalone solution into a fully integrated application with enhanced features to support clinical workflows. The goal was to create a seamless, scalable platform that could deliver accurate risk assessments, provide real-time monitoring and ensure a user-friendly experience for healthcare professionals. To achieve this, Revealix required specific technical expertise to build a secure, high-performing and reliable solution.

SOLUTION & OUTCOME

GAP played a key role in transforming Revealix’s AI model into a fully integrated, feature-rich application. By building essential application components around the original machine learning model, GAP helped turn Revealix’s technology into a complete solution that enables real-time foot health assessments for patients with diabetes.

GAP optimized Revealix’s ML models, improving their efficiency and data processing capabilities to enhance predictive accuracy. The AI-powered solution processes thermal imaging data to assess the risk of diabetes-related foot complications, providing clinical observations that help healthcare professionals take proactive measures. While the AI model does not offer a medical diagnosis, its insights allow providers to standardize and automate foot health assessments, improving patient outcomes.

Beyond AI model optimization, GAP continues to provide critical development and support services, including software quality engineering, UI/UX design, Swift development, project management and DevOps. This ongoing collaboration ensures Revealix’s Foot Health as a Service platform remains scalable, secure and effective.

With GAP’s expertise, Revealix is expanding its impact beyond diabetic care, offering new use cases in general foot health and athletics. By leveraging AI and automation, the platform helps clinical teams streamline workflows, scale preventive care practices and ultimately reduce the risk of amputations caused by diabetes-related complications.

ADDITIONAL PROJECTS

VIEW ALL PROJECTS

NZero

NZero

GAP supports nZero with AI-powered carbon tracking, optimizing energy use and helping organizations achieve net-zero sustainability goals.

More Info
ManageAmerica

ManageAmerica

GAP developed ManageAmerica’s mobile app with Flutter, enhancing tenant payment processes and improving user engagement in property management.

More Info

RELATED ARTICLES

VIEW ALL ARTICLES

Your Healthcare AI Model Passed the Test. The System Still Isn’t Ready.

August 17, 2026

Machine Learning & AI

Your Healthcare AI Model Passed the Test. The System Still Isn’t Ready.

Your healthcare AI model cleared validation. Its performance looked strong, the pilot produced the expected results and stakeholders saw enough promise to start talking about production. Then the environment changed. The model had to work with information coming from multiple systems instead of a controlled dataset. It had to operate

Read More
Security and Observability: What They Actually Mean, and Why They Go Together

August 10, 2026

DevSecOps

Security and Observability: What They Actually Mean, and Why They Go Together

The average data breach in 2025 took 181 days to identify, and another 60 days to contain once it was found (a total lifecycle of 241 days), according to IBM. Organisations spent roughly six months just figuring out whether a breach had happened at all. The same report put the global

Read More
About Gap
Overview
Services
Services
Industries
Insights
Insights