There's No Headcount Solution to AI-Powered Abuse: Introducing Cinder's AI Agent Platform

For years, the standard playbook for platform safety was straightforward: hire reviewers, write policies, build queues, and repeat. If abuse spiked, you scaled the team. If a new harm type emerged, you added a workflow. Larger platforms layered in AI for detection at the edges, but the foundation was still human operations, and the model was still fundamentally linear.

Then generative AI gave bad actors the ability to operate at machine scale, and the linear model collapsed.

Bad actors can now generate harmful content at a scale and sophistication that simply wasn't possible a few years ago. The problem is growing exponentially, not linearly, and there's no headcount solution to an exponential problem. That's the challenge Cinder Agents were built for.

What are Cinder Agents?

Cinder Agents are AI systems trained on your content, your policies, and the validated decisions your own team makes over time. They're deployable against your most pressing problem from day one, and they get measurably sharper with every review cycle as your ground truth dataset grows. Every time a human reviewer makes a call, the agent learns from it.

The result is an agent that enforces your standards at machine scale, with the context and judgment that comes from understanding your platform specifically. Each decision comes with a confidence score and human-readable reasoning, so your team isn't left seeing a decision that came out of a black box.

The system runs as a continuous loop. Agents classify new content in real time with faster latency and auto-resolve the high-confidence cases. Anything in the gray area gets escalated to the right human reviewer with full context and a recommended action already attached. Your team stays in control, spending their time on the decisions that actually require judgment. Every review feeds back into the next retraining cycle, new agent versions are shadow-tested in production before deployment, and your agent next month is meaningfully sharper than your agent today. This is what it looks like when moderation quality becomes a metric you can actually move.

An Agent for Every Problem

One of the things we've learned working with companies like OpenAI, Character.AI, ElevenLabs, Midjourney, BeReal, and Depop is that platform safety problems don't come in one shape. What a marketplace worries about looks nothing like what a dating app or a foundation model lab is concerned with. So we didn't build a static agent. We built an architecture that lets you deploy dynamically updated agents for your specific problem, trained on your policies and tuned to the context of your platform.

Content moderation. Whether your team thinks in terms of a hate speech agent, a CSAM agent, or a harassment agent, Cinder's content moderation architecture covers the full spectrum and trains each enforcement layer on your specific policies, not a generic taxonomy that treats every platform the same. It handles text, image, audio, video, and live stream in one system, with sub-500ms latency so harmful content is stopped before it reaches your users. It covers hate speech, CSAM, NCII, extremism, harassment, scams, and AI-generated harm, and it writes what it learns from your team's reviews back into the system that runs your operation.

Learn more about the content moderation agent.

Case investigation. Coordinated abuse rarely shows up as a single clean violation. It moves across accounts, content, reports, devices, payments, and time, and a queue item can show you the latest flag without revealing the pattern behind it. The case investigation agent pulls accounts, content, behaviors, prior decisions, and related entities into one structured view, surfaces the pattern, and resolves the cases it can with confidence. The ones that need human judgment go to the right reviewer with full context and a recommended action already attached. Every resolved case becomes part of the intelligence layer that helps the next one move faster.

Learn more about case investigation.

Counterfeit and IP protection. Counterfeits erode trust and drive down what legitimate sellers can charge. DMCA notices pile up faster than most legal teams can answer. Brand impersonation siphons customers into scams that look exactly like you, and the trust cost compounds in ways you can't fully refund. The IP and copyright agent is trained on your platform's specific fraud signatures and pulls external context from rights-holder databases, marketplace cross-listings, reverse image lookups, and web signals to make calls that a static classifier never could. Every action is documented and defensible, before a claim becomes a chargeback or a customer who never comes back.

Learn more about IP and copyright protection.

User fraud and ATO. Fake accounts, synthetic identities, account takeovers, and bot rings don't announce themselves cleanly. They show up as patterns across accounts, devices, events, relationships, and history. The fraud agent connects those signals and evaluates them against your policies and your platform's specific fraud signatures rather than generic models that bad actors already know how to evade. Suspicious behavior is detected before it spreads, with the surrounding evidence and reasoning your team needs to act quickly.

Learn more about fraud and ATO detection.

Custom agents. Custom agents run on the same architecture as everything else in Cinder. Same policy configuration, same observability, same closed-loop retraining, same human escalation paths. The difference is that you define the problem. Whether that's avatar misuse, romance scams, election interference, or something no vendor has a name for yet, you wire your policies and your data into an agent built for the exact risk your platform faces, and run it on the same platform already powering your operation. You can build one independently or work with our services team to design and train it alongside your operators.

Learn more about custom agents.

What AI Agents Look Like in Practice

Across our customer base, Cinder protects more than 3 billion users, automates 94% of human review, and processes over 400 million events daily.

Teams come to Cinder needing agents that can scale with their product without losing the judgment their policies require. We've been able to deliver that by training every agent on the decisions a platform's own team has already made, so enforcement stays aligned with their standards as volume grows, and gets sharper with every review cycle rather than drifting from it.

For example, Character.AI needed a way to enforce policy on AI-generated personas at scale, including detecting whether characters were modeled on real people, living or deceased, and routing those cases appropriately. With Cinder, they reduced their human moderation queue by more than 50% while maintaining the quality bar their team had set. Their reviewers didn't disappear. They moved up the stack, focusing on the cases that genuinely require human judgment rather than spending their time on decisions an agent could make with high confidence.

Black Forest Labs came to us before launching FLUX and needed adversarial testing rigorous enough to match their release velocity. Cinder Agents stress-tested the model against real-world attack patterns, surfaced edge cases before they became public incidents, and helped them launch 10x safer than benchmark industry models, with a greater than 90% reduction in CSAM and NCII vulnerability.

Zello, a push-to-talk platform used by first responders and field teams, needed to catch repeat offenders faster across a high-volume, real-time environment. With Cinder, they now take down 3x more repeat-offender accounts, and 50% of all bans are executed automatically through orchestrated agent workflows without human intervention on each individual decision.

One entertainment streaming customer launched a major new messaging feature and had the full safety infrastructure in place within two weeks, a deployment that would historically have taken months of internal engineering work. That speed matters when you're introducing a new surface that bad actors will probe from day one.

Agents That Scale Your Team

Cinder Agents handle the decisions that don't need a human, so that the decisions that do get the attention they deserve. Every agent is built with human review in the loop by design, not as a fallback. Your team sets the policies, reviews the edge cases, and every call they make sharpens the agent for the next one. The agents get the volume. Your reviewers get the cases that actually matter.

The trust and safety teams we've worked with closest don't talk about this as automation. They talk about it as finally having enough leverage to do the job well.

If you're dealing with abuse at scale and wondering whether your current approach can keep up, we'd like to show you what Cinder Agents can do for your platform.

Explore Cinder Agents.