How Zentropi partners with Character.ai

Character.ai takes safety seriously. With millions of users creating and chatting with AI characters every day, the team invests heavily in systems that help protect their community — and they're always looking for ways to raise the bar.

Character.ai's Platform

Character.ai's platform is unique. Users create AI characters with custom descriptions and definitions that then have open-ended conversations with users. The content is dynamic, context-dependent, and shaped by the interaction between a character and the user. Roleplay is a core part of the product, and Character.ai's safety classification models help prevent conversations that violate Character.ai's policies.

Classification quality matters

Safety classification is only as good as the tools behind it. Traditional, off-the-shelf safety classifiers carry strong priors from their own training. A general-purpose model might flag language that is perfectly fine in one context but not another because it was never trained to understand that context matters. 

By contrast, Zentropi's approach separates the policy from the model. Zentropi's customers write policies in plain language that describe exactly what they're looking for, and Zentropi evaluates content against those policies at inference time. 

“Working in trust and safety is an ever evolving and incredibly varied field. Having flexibility and specificity is incredibly important to serve a good experience to our userbase. Zentropi has been a great partner.”

- Nathan Berl (T&S Manager, Character.ai)

This unlocked several powerful capabilities:

Context-aware accuracy. Policies encode the distinction between fictional and real-world content directly. The result is fewer false positives on legitimate interactions and more visibility on genuinely problematic content.

Multi-turn precision. One of the hardest classification challenges is assessing the last message in a long conversation: did it comply with the policy, given everything that came before? Off-the-shelf models struggle here — they get distracted by earlier turns and can't accurately isolate the final message. Zentropi's CoPE model handles this because the policy directs the classifier's attention precisely where it needs to go. This is one of the clearest demonstrations of what policy steerability buys you.

Rapid adaptation. Safety is an adversarial domain. Users find new evasion approaches constantly, and language evolves as new cultural references and fictional universes emerge. With Zentropi’s steerability, emergent vulnerabilities and exploits can be quickly reduced.

Iterative refinement. Zentropi enables teams to test harm awareness against real examples, see where it breaks, refine the language, and re-test — all in a tight loop. Safety expertise that used to live in the heads of a few specialists gets encoded in policies anyone can read, test, and improve — and the team's capacity to cover new categories can scale like never before.

Better classification, better outcomes

Any company whose content involves context, nuance, or domain-specific conventions will eventually find that general-purpose classifiers aren't enough. That’s where Zentropi can help. Policy-steerable classification means your system can more accurately follow your rules– an advantage that compounds through the entire safety stack. 

If you are an AI-powered platform that shares these challenges, get in touch with us at info@zentropi.ai.