The Checkstep Platform Spotlight is our regular deep dive into the features that power the Checkstep AI content moderation platform. Last time, we put the spotlight on Policies and our automation tool, ModBot. This time, we're exploring how we manage real-time chat moderation at scale.

A tough challenge

Moderating live chat is one of the most demanding problems in Trust and Safety. Unlike post-hoc content review, real-time moderation is a priori: problematic messages must be caught and blocked before they are ever seen. This means applying your full moderation policy in under 200ms, across multiple languages and geographic regions, without degrading the user experience.

The cost of getting it wrong cuts both ways. Too permissive, and you expose your users to harmful content, a concern that is especially critical for platforms with younger audiences or high risk users. Too strict, and legitimate messages get blocked, creating frustration and eroding trust in your community. Real-time moderation done right makes all of this invisible.

The best speed/accuracy trade-off

One of the AI strategies integrated into Checkstep that is ideally suited to real-time chat is our fine-tuned Cohere embeddings model. Embeddings work by mapping the meaning of a message into a mathematical space, making classification language-agnostic and robust to paraphrasing. Within the Checkstep moderation UI, you define your labels and provide a handful of examples for each. Within minutes, new messages are classified against those samples.

What makes this approach powerful is that the model doesn't just memorise your examples. It learns a broader sense of what "harmful" looks like in that space, meaning it can surface messages that fall outside your labelled categories but still share the same character as content you have flagged before, without any additional training data.

Understanding context

Two identical messages can mean very different things depending on the conversation they belong to. Take this exchange:

Alice: It is getting dark
James: Let me plug my lamp
James: Just Turned On
James: How is it now?

"Just Turned On" in isolation might trigger a moderation flag, but read in context it is clearly about a lamp. Checkstep's policy violation detection analyses each message alongside its conversational history, ensuring your moderation reflects what was actually meant -rather than reacting to words out of context.

Global coverage, local speed

Checkstep operates edge servers across multiple regions worldwide, with the ability to deploy anywhere AWS operates, typically within half a day. Wherever your community lives, you can expect fast, consistent and reliable moderation

See it in action

Want to go deeper? Read the full chat moderation documentation or book a demo to see it in action.