Engineering

Since our founding in 2003, we have built software, developed algorithms, researched core technologies, and operated systems. Some of these systems have run reliably for more than 20 years.

Distribution

We have distributed our own apps worldwide since 2016.

Expanded to 39 languages and maintained translations
Built and operated our app store publishing infrastructure
Monetized apps through purchases and advertising
Measured and analyzed usage

39 languages / purchases in 52 countries and regions using 36 currencies (since 2016)

Core Stack

Security 2017–

Security Evaluation
Intrusion Detection
Audit
Key Management (CKMS)

Mobile Distribution 2016–

iOS
Android
Swift
Kotlin
Store Operations
Localization
macOS

Embedded & OS 2008–

Linux
C/C++
AOSP
Yocto
Virtualization
Windows

Networking & Protocols 2004–

HTTPS Proxy
Device Management Protocol
Network Performance
Wi-Fi Log Analysis
Rust

Business & Commerce Systems 2004–

Web Systems
Payments
CMS
Database Engineering
PHP
TypeScript
Node.js
AWS
GCP
Cloudflare

Imaging, Documents & Language 2003–

Image Processing
OCR
Full-Text Search
Algorithm Design
LLM
Vision Models
Python

Technology Decisions

We have worked in mobile development since the iPhone 3GS era in 2009 and have distributed our own apps since 2016. The iPhone showed what was possible: a powerful handheld device connected to the internet could deliver services around the world.

Usage data from our own apps lets us evaluate technology and UI/UX continuously in real-world conditions. It also lets us test at a scale that individual projects rarely reach. We apply what we learn to design work beyond mobile.

In 2016, we compared Xamarin, Cordova, and React Native through hands-on implementation, and we have evaluated Flutter since its public release. Today, we focus on native development in Swift and Kotlin and have used SwiftUI extensively in production since iOS 15.

Working close to the OS and hardware lets us optimize both measured performance and perceived responsiveness in rendering and camera work. We can also use new OS features and C/C++ libraries without waiting for an abstraction layer to catch up. Long-running apps are also less affected by breaking changes in cross-platform frameworks.

Device Control

Barcode-Based Device Readout

Instrument readings are collected via BLE, Wi-Fi, or barcode.

The instrument transmits readings wirelessly and displays them as a barcode on its LCD in real time. The app scans the barcode with its camera and records the readings.

Swift
AVFoundation
BLE
Wi-Fi
Barcode

On-Device AI

On-Device AR Recognition

Objects in the camera view are recognized on the device and labeled in the AR view.

Each label is anchored to its object's position and stays over the object as the device moves.

Swift
ARKit
On-Device ML