UI/UX Design, iOS Development, Android Development, API Development


Cloud: Rackspace

Operating system: Linux ubuntu server

Languages and runtimes: Java

Framework: Spring Framework

Database: Microsoft SQL Server


iOS app: Objective-C


Vivid is a commercial cleaning company, handling 1000s of sites in Australia.

THE PROBLEM: A small group of their cleaners were “gaming” the system by sending unverified friends and family to do the cleaning instead of the authorised contractor. The major issue with this was that Vivid looks after some high-profile sites, most of which have specific compliance requirements for individuals to be allowed on site.

THE NEED: They wanted to find a way to better track the cleaners as they come on and off sites so that they could weed out the “bad players”. At the same time, they also wanted to make the whole cleaning process more efficient. All this was required while plugging into their existing in-house system.

Our Process

1. Devwiz worked closely with the Vivid team to understand their current technology and cleaning service processes.

2. Put together a software plan and road map that outlined our full proposed solution of creating a mobile app with facial recognition. This included a plan for getting an MVP up and running quickly, outlining sprint timelines and deliverables linked to milestone payments;

3. Created a front-end app design in line with their brand strategy and designed for their specific user persona: the cleaner.

4. We developed the CleanTrak app in line with the approved designs and provided the client’s team with regular updates and apps to test via TestFlight and Google Play (beta testing);

5. We performed internal QA and helped the Cleatrak team get the most from their testers using tools like Trello.

6. Not only do we now support them with a maintenance agreement, but there have also been many new iterations and development phases since the initial build.


By the end of the development phase, Vivid had iOS and Android apps that linked directly to their internal systems. These apps allowed Vivid to:

  • Instantly check cleaners checking to sites against their verified photos and flag any suspicious activity, all using facial recognition;
  • Manage checkins and checkouts easily using the implementation of QR codes;
  • Provide specific site information via APIs;
  • Collect specific info like garbage weights and site photos, all within the app.

As soon as this solution was implemented, Vivid was able to see which cleaners were gaming the system and either train them up or eventually let them go. This meant that they were magnitudes more compliant with their clients.

The app also had an add-on effect in that cleaners were now much more efficient. A combination of the timer and custom in-app instructions meant that sites were cleaned quicker and to a higher standard.