AI Agents
Cinder agents are trained on your content, your policies, and your team's validated decisions. They enforce your standards at machine scale and get measurably sharper with every human review. Moderation quality becomes a metric you can actually move.
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
Proactively protect against CSAM, extremism, and other forms of policy-violating content in real time.
Disrupt coordinated abuse faster by pulling accounts, content, and behaviors into one structured case view.
Respond to DMCA requests and protect your customers and brand from scams and fraud.
Stop fake accounts, fake applicants, and account takeovers based on your platform's specific fraud signatures, not just generic patterns.
Configure and launch an agent around any policy, guideline, or standard your team faces. Cinder tackles every edge case.
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.
“Cinder provided rigorous adversarial testing that matched our release velocity. Their team found important edge cases and helped us address them before launch.”
Ben Brooks, Head of Public PolicyBlack Forest Labs
