Case study · Scandit · 2022 – 2024

AR shelf scanning in SwiftUI, on a C/C++ vision core

At computer-vision company Scandit I built a SwiftUI augmented-reality shelf-scanning solution from scratch — and worked across the stack it sits on, from the C/C++ SDK core to its WebAssembly build.

Role
Mobile Developer
Where
Remote (Berlin)
Outcome
AR shelf scanning, built from scratch in SwiftUI
  • SwiftUI
  • ARKit
  • C/C++
  • Computer vision
  • WebAssembly
AR overlays highlighting low-stock and out-of-stock products on a retail shelf
Shelf intelligence as Scandit presents it publicly — imagery from Scandit's marketing.

The setting

Scandit builds one of the leading computer-vision SDKs for smart data capture — the technology that lets a phone camera read dozens of barcodes, labels, and shelves in real time. Their public shelf-intelligence products help retailers spot out-of-stocks and pricing issues by pointing a device at a shelf.

I joined to work on that capture stack across mobile platforms.

What I built

  • An AR shelf-scanning solution, from scratch — a SwiftUI application layer that turns live camera capture into augmented-reality overlays on the shelf itself, so what the computer vision finds is readable in place, at a glance.
  • SDK integration and maintenance — integrated and maintained the Scandit SDK across mobile targets and its WebAssembly build, keeping capture behavior consistent across platforms.
  • C/C++ core contributions — worked inside the SDK’s C/C++ core, the layer where camera frames meet the vision algorithms.

This was commercial SDK work, so specifics stay at the level Scandit shares publicly — the links above show the product space.

Why it matters

This is the rarest combination I offer: modern SwiftUI on top, augmented reality in the middle, and a cross-platform C/C++ computer-vision engine underneath — the full path from photons hitting a camera sensor to a decision a person can act on.