AI Agents
Cinder agents are trained on your policies, examples, and validated decisions. They resolve clear cases, escalate hard calls with context, and make every action inspectable across content, prompts, outputs, cases, and safety workflows.
Overview
Off-the-shelf classifiers are trained on someone else's data. They miss the edge cases that matter most to your platform and over-flag the ones that do not.
Cinder agents are different. We train continuously on the policies your team writes, the decisions your team makes, and the context your users live in. Every human review sharpens your agents. Every retraining cycle compounds accuracy. Your reviewers handle the nuance. Your agents learn and act at scale.
Agent types
Enforce policy across posts, comments, messages, uploads, streams, prompts, and outputs, with clear cases handled fast and edge cases escalated.
Pull accounts, content, prompts, outputs, reports, and behavior into one structured case view for coordinated abuse and high-risk escalations.
Screen AI interactions, model outputs, tool calls, and multimodal content against your policies before risky behavior reaches users.
Turn policies, red-team findings, evals, and launch criteria into workflows your safety team can inspect, measure, and improve.
Configure an agent around the exact decision your team needs to operate: chats, streams, reports, appeals, child-safety signals, prompts, outputs, or policy QA.
Agent loop
Cinder agents work alongside your team and get sharper with every review. The same QA system that measures your human reviewers now measures your agents too.
01
Agents classify new content proactively, with sub-second latency at the edge. Each one is context-aware of content type, user history, and policy nuance. Every decision includes a confidence score with human-readable reasoning.
02
Agents auto-resolve high-confidence cases. Proactive alerting escalates edge cases to the right human reviewer. Your team stays in control, with the full context of every decision at their fingertips.
03
Every decision is logged with agentic reasoning. Confusion matrices, precision/recall metrics, and side-by-side agent benchmarking are built in. Your team can audit and override anything your agents do.
04
Your team's reviews feed agent retraining automatically. New versions are benchmarked against golden sets, shadow-tested in production, and deployed within days. Accuracy compounds as your ground truth dataset grows.
“What Synthesia needs now is infrastructure that can keep pace with how quickly the product and the threat landscape are moving, which is why Synthesia is combining its know-how with Cinder's technological capabilities.”
Synthesia
