CINDER · 2026
Case study
How Character.ai Reduces Human Moderation with Cinder
A high-volume AI platform used Cinder agents to classify user reports across live traffic and backlog cases, reducing queue pressure without compromising decision quality.
3 MIN READ
Overview
How Character.ai Reduces Human Moderation with Cinder
Character.ai partnered with Cinder to improve automated decision making as a component of their moderation strategy. Cinder’s agentic review reduced a significant percentage of cases, allowing moderators to focus on high-risk cases requiring human judgment.
Challenge
Challenge
Character.ai has a significant volume of user reports, including many that require moderators to classify the reported content.
Solution
Solution
Character implemented Cinder Agents to classify user-reported content. The deployment included:
Automated Classification: AI agents evaluated reports to determine policy violations versus false positives
High Confidence Thresholds: Conservative confidence settings minimized both false positives (incorrectly flagged content) and false negatives (missed violations)
Dual Application: The system processed both live incoming traffic and existing backlog cases
Pattern Recognition: Cinder AI agents identified characteristics across both violating and non-violating content to improve accuracy
Results
The implementation delivered substantial operational improvements:
More than half of all queues were reduced across the largest moderation backlog while maintaining decision quality SLA
Significant reduction in hours saved in human review time
Focused human effort redirected to high-confidence violations and ambiguous edge cases
Improved prioritization of highest-risk content requiring immediate attention
Impact
Impact
By "shrinking the haystack," Cinder enables Character's human moderation team to operate more strategically. Human moderators now concentrate on complex cases where human judgment adds value, while AI handles routine classification decisions with high accuracy.
Future Development
Future Development
Character.ai and Cinder continue expanding their partnership to address novel harm type detection and emerging user safety risks.
This agentic workflow demonstrates how AI augmentation can force multiply trust and safety operations, reduce operational costs, improve response times, and increase moderator job satisfaction by focusing human moderation time on the cases that matter most.
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