When we first started working with Recolor, we were struck by how vibrant and diverse their online community was. Being the most popular coloring book on mobile, they not only provide a creative and stress-relieving environment for their users, but they also provide a platform where users can interact with one another. The Oterlu team’s aim was to empower Recolor’s community management team to continue growing their diverse community whilst keeping their users safe in a scalable way.

The first step in our process was to do a deep dive with the community management team to understand their needs and the needs of the user base. Their team knows the Recolor community inside out, and they are also responsible for determining what type of behavior is and isn’t allowed on their platform. We found collaborating with the community management team to be the best way to go because online communities are not homogenous – they all differ to some extent – and understanding the particular rules for the platform is important (for example, whether or not swearing is allowed on the platform.)

After we conducted the deep dive, we had a pretty clear picture of the community itself and of the needs of the management team. We then set about customizing our machine learning model to fit Recolor’s community. We started off with a baseline language model that understands language and then learns how language is used on Recolor, such as the use of slang expressions. Next, we tuned the model to search for some of the nuanced policies specific for the platform.

Model structure – The AI models first learn how language is structured, then how users communicate within the game and finally what is allowed and not allowed.

Once we trained the customized machine learning model, the next step was to use it in a live environment. For this we made the model available through an API. We also built our infrastructure in a way so that it could scale with unpredictable traffic loads, like spikes in user activity. This means that we today can handle all sizes of customer requirements, ranging from a couple of thousand messages to a billion messages a day.

The final focal point of our service, aimed to help the management team in their work both on a daily basis and in the long time horizon, was providing access to the Oterlu Community tool. This tool allows the team to truly harness the power of our machine learning models. It provides analytics of what is going on in the day-to-day environment, such as which time zones were seeing peaks in specific policy violations.

Today, our service covers the global Recolor community and is able to provide real-time feedback on when a user is committing a policy violation.