Technology

At Nect we are proud of the quality of our products, especially the ease of use of our apps! Do not be fooled: Below the easily accessible user experience is a complex system that fulfills various security, compliance, and technological requirements.

Our Android and iOS apps are the main interaction point for our users. We also provide a lot of tools for internal use and for our customers to interact with us. At the core of our value chain is a Machine learning system, that uses state of the art methods to establish face and age recognition as well as many more methods of this highly interesting field. These services are orchestrated by our service-oriented backend system. All this runs on our hardware on a multi-datacenter kubernetes cluster

Our Tech-Deparment at a glance:

Frontend

Our product can only work well, when it is easy to understand and comfortable to use. Daily, we strive to generate great usabilty. Our highly motivated team of mobile, iOS and Android developers are continiously working on building a state of the art product that will coninue to win lots of awards.

Tech Stack Frontend (Mobile Team)

Swift und Kotlin

Swift und Kotlin. We build our mobile apps using a fully native tech stack, using Swift for our iOS app, and Kotlin for our Android app.

Apple Vision

We use the native Computer Vision iOS library to make the document capture process easier and friendlier for the user.

Google ML Kit

The native Android libraries are used to make the identification process more user-friendly.

4NEU Frontend OpenCV (Mobile Team)

We rely on this library for additional image-processing tasks in the Android app.

Tech Stack Frontend (Web Team)

TypeScript

We build our website and web apps using TypeScript for added type safety and quality.

React

Our projects use React on the client-side to build UI's and handle data binding and user interaction.

Next.js

We take advantage of the out-of-the-box capabilities of Next.js, especially regarding server-side rendering and static site generation.

styled-components

Our internal Component Library is styled using CSS-in-JS through styled-components.

NFC

We use the mobile devices' NFC capabilities to make the identification process faster and more convenient.

Backend & Infrastructure

We are a small team of enthusiastic and experienced software developers, with the goal to provide our customers and colleagues with a secure and functioning IT infrastructure. Our main strength is our problem solving ability amd flexibility, as it can get messy when a pragmatic solution needs to be found.

Golang

Used for development of our Backend services and all kind of devops tooling.

CockroachDB

PG-like Relational Database which easily scales and offers an active-active HA setup.

MinIO

S3-like Object Storage for scaling to billions of files.

Kubernetes

Orchestrates all our workloads over all our datacenters.

Docker

Container Engine to package up our workloads.

Computer Vision & Machine Learning

At the core of our technology works our inspiring computer vision / maschine learning team. Here, we conceptualise and implement the algorithm which enables our automatic identifying solution and makes it so safe to begin with. We use state of the art methods to establish face and age recognition as well as many more methods of the highly interesting field og machine learning. Experts in machine learning and scientists find their professional home here, to use their expertise and combine scientific findings with practical know-how to bring into a product based context.