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I envisioned a mobile app development company that takes a radically different approach to building apps.

How We Create Beautiful Mobile Apps in Half the Time

Here’s the story of why I created Foxbox Digital, a different kind of mobile app development company.

 we needed to build a mobile app to complement our existing web app. We were a team of web developers and didn’t have experience building iOS or Android apps. We reached out to some local app development companies in Chicago to get quotes on what it would take to build the app. The quotes were really high, and that was for 

. And after that, we’d need to have them build the Android app too.

We’re responsible for hosting and supporting the API that actually runs the app. We ended up finding a freelance iOS developer to build the app, and he did a killer job. It cost a ton of money though, and at the end of the day we could only support half our users because the other half were on Android.

 I understand why mobile app development companies charge so much for a mobile app — developers are expensive, and in-demand. But isn’t there a better way?

, Ionic, and Cordova. Ionic and Cordova apps look like native mobile apps, but they don’t feel like them — they’re slow and sluggish. React Native strikes the perfect balance because they’re fast to build, and the apps look and feel like native mobile apps (snappy and responsive). And it’s simply a joy to program the code. 

With a cross-platform framework like React Native, we build both the iOS app and the Android app at the same time. 

This cuts the development time (and cost) in half

Through our engagement with Home Chef, we developed and published both iOS and Android apps using React Native.

This helped them significantly increase app store ratings, meal kit conversions, and user engagement rates. 

The mobile app is only part of the solution though — every mobile app needs an API to power the app. An API is the server and software that the app communicates with in order to do what it needs to do (i.e. get and store data). Once you have an API built, you need to host and maintain it. As a client, do you want to be bothered to maintain an API?

I thought about all of the things that you need to do in order to keep your app up and running in the app stores.

Mobile App — build it, launch it, maintain it (fix bugs and security issues).

API — build it, build servers to host it, maintain it.

Monitor the API to ensure it stays running and respond to issues as they come up.

Upgrade the mobile app every time Apple or Google releases a new phone or operating system. Re-release to both app stores.

Do clients really want to do all of this?

 We’d use reusable building blocks to create the core of the app, and a finely tuned process to develop them. With this approach, it’s much easier to predict how long it will take to build the app.

 that handles everything from design to building clients’ apps to keeping them up & running, for the life of the apps. We’d be a trusted partner to our clients, so they wouldn’t have to worry about anything involved in building or running their mobile apps.

Foxbox Digital creates beautiful cross-platform mobile apps  in half the time. We handle everything involved in running and maintaining your app, for the life of the app.

 and let's come up with a strategy to build it.

Discover how K Health partnered with Foxbox to scale its AI healthcare platform across systems using Kotlin Multiplatform—powering personalized, cross-platform care at scale.

AI Healthcare Platform Case Study | K Health & Foxbox Digital

When K Health was founded in 2016, the company set out to transform primary care. In 2024, they underwent a transformation of their own. One that required a highly capable engineering partner to bring to fruition.



Amid steady growth and even empirical evidence that K Health’s AI Physician Mode outperforms human doctors, the company set out to evolve the platform from a single-instance patient app into a white-label virtual care platform that allows hospitals and health systems to deliver personalized, AI-powered care at scale. 

With insufficient internal bandwidth to simultaneously build toward the future and maintain existing functionality, K Health needed a trusted partner to extend their engineering capabilities immediately

“We needed a team tomorrow or we weren’t going to get it done in time.” - 

Zachary Siegel, VP of Engineering, K Health

K Health turned to Foxbox for the support necessary to rebuild the apps from scratch, natively in iOS and Android, optimizing for cross platform functionality and adaptability via Kotlin Multiplatform. With a focus on AI functionality, automated testing and scalability, our team hit the ground running as extensions of the K Health team.

At the heart of the K Health experience is an AI-powered engine for symptom checking, triage, and scheduling—used by both patients and providers. Our role was to ensure that this AI could be delivered in a way that was clear, responsive, and trustworthy. That meant building the connective tissue between AI and UI.

We also helped define and build core components of the front-end experience, developing interfaces that could adapt to new partners, new conditions, and new markets without starting from scratch.

Adopting Kotlin Multiplatform allowed us to build native iOS and Android apps with a shared, secure business logic. This shift dramatically improved scalability by reducing duplicate effort across platforms.

 With Kotlin Multiplatform, we delivered fully native UI layers without compromising performance, resulting in lightning-fast interactions, smooth animations, and rock-solid functionality. 

We engineered for the future: optimizing for low-bandwidth environments, supporting multiple languages, and building a custom QA automation framework to validate daily releases and minimize regression risk. Every decision was made to enhance performance and long-term resilience.

“We validated every day… it really helps enhance the testing experience.”

Our approach goes beyond writing code. From day one, we operated with full product ownership—proactively flagging issues, suggesting solutions, and adapting to shifting specs and timelines. We worked side-by-side with K Health’s internal engineers, designers, and product leads, navigating complexity with confidence and clarity.


This close collaboration enabled K Health to roll out new branded apps for major US-based health systems to provide AI-powered virtual care solutions, all while maintaining a single codebase and unified release pipeline. Because the architecture is built for flexibility, new deployments don’t require rework. Localization, customization, and expansion are possible on the fly. If a clinic wanted to launch in French tomorrow, they can do so without changing the code.

“They see something, they say something. They write it up, explore it, and suggest solutions.”

 — Zachary Siegel, VP of Engineering, K Health

Today the K Health platform is powering virtual care programs at health systems that serve over 15 million patients.
All without sacrificing development efficiency, code quality or the ability to adapt. Together, our partnership doesn’t just extend capacity. It empowers K Health to move faster, think bigger, and scale smarter. And we’re proud to be along for the ride.


“The learning curve was steep, but now we can confidently

build anything with Kotlin Multiplatform.” — 

K Health: Powering AI-Driven Healthcare with a Personalized Cross-Platform Experience

Learn how to use Claude Code to automate end-to-end test creation. Build, review, and run automated tests with Claude Code, reducing setup time, enforcing project rules, and improving test coverage with human oversight.

Automated QA Testing in Claude Code | Foxbox Digital

AI coding assistants are no longer just for brainstorming or writing boilerplate code. They’re becoming powerful tools in test automation. In this piece we’ll walk through a real QA workflow using Claude to speed up repetitive test creation, highlight the human/AI handoff points, and share lessons learned along the way.

After an initial healthy skepticism, I’m now a firm believer that AI tools can dramatically improve QA workflows. Automated testing powered by tools like Claude can reduce hours of manual setup into minutes. But it’s not “end-to-end automation.” Human expertise is still critical in order to:

Architect processes before they can be automated.

Identify the right balance between reusability and project-specific rules.

Review and refine AI-generated code for quality, security, and maintainability.

Using tools like Claude Code, the QA engineer’s role shifts from operating a repetitive system to designing a new one entirely. The payoff is dramatic — a process that once took 1–2 hours manually can now be completed in 20–30 minutes with the right approach.

Let’s walk through one test case for a telehealth application with an AI symptom checker. To confirm patients are able to get the answers they need, we need to confirm they can upload a photo during an appointment set up flow.

Manually, the process would usually look something like:

Validate the app doesn’t crash and the upload succeeds.

Running this flow manually is repetitive and time-consuming

Instead of hand-coding every selector and step, Claude can act as your automation assistant at every step of the way. Not too early in the process, not tacked on to the end. The craft is now finding balance. Here’s how I approached it

Using a recorder tool, the QA clicks through the scenario. Each action (tap, input, upload) is captured into a Java file.

Comments mark each step (e.g., // Login, // Select symptom, // Upload photo). Claude uses these annotations to understand context.

Naming conventions (PascalCase, file structures).

When to reuse existing methods (e.g., login flows).

Claude ingests the recordings + rules, and generates test files:

Page object files (for selectors and UI elements).

The engineer reviews the generated files, validates selectors, and fixes any missteps. Common pitfalls:

Misclassified selectors (e.g., ID vs XPath).

Fragile selectors that change with app updates.Security risks (Claude sometimes proposes hard-coded values).

With experience, teams learn to anticipate these errors and adjust rules accordingly.

On this particular project, this process has reduced setup time by ~70%, improved reusability, and has helped us deliver a better product in less time. Still, success requires careful upfront architecture and thorough human oversight, since selectors often trip up the AI and rules must be rebuilt for each new project. It moves at a speed no human could, but also makes mistakes that humans never would.

Understanding how to navigate both realities is what will redefine the “Quality” in QA.

: Begin with simple, repetitive flows like logins, file uploads, or form submissions—these are perfect for automation because they’re high-frequency and low-risk. Starting here helps you validate the process, refine your rules, and build confidence in Claude before tackling more complex scenarios.

Your CLAUDE.md (or equivalent rules file) acts as the brain of the system, guiding Claude on naming conventions, file structures, and method reuse. The clearer and more prescriptive this file is, the less cleanup you’ll face later, making it one of the highest-leverage investments in your QA automation setup.

Claude moves fast, but it’s not flawless—misclassified selectors, fragile locators, or incorrect assumptions will creep in. By budgeting time for debugging, you turn these missteps into learning moments that strengthen both your test suite and your rules over time.

Never merge AI-generated code blindly; human validation is critical. Security is a particular concern—watch closely for leaked credentials, hard-coded values, or unsafe shortcuts that might slip through without careful review.

As you run more scenarios, your rule set will grow into a library of reusable steps, making Claude more accurate and reducing duplication. Over time, this library becomes a strategic asset—accelerating test creation and improving consistency across your QA practice.

Inside the Box

Articles, updates, and ideas you can put to use.

How We Create Beautiful Mobile Apps in Half the Time

K Health: Powering AI-Driven Healthcare with a Personalized Cross-Platform Experience

Automated QA Testing in Claude Code