Product: Moderation

See what manual content moderation costs at 1M, 10M, and 100M items a month vs. Stream AI Moderation, plus customer savings and Stream's pricing.

Keyword lists miss disguised payment requests and block honest buyers. See how to catch off-platform payments by reading intent and context.

We analyzed chat and moderation data from two top prediction markets during the 2026 World Series. Volume moved, but the mix of harms stayed the same.

Live video does not give you a queue. A frame that leaves the camera on WebRTC is on someone else's screen in a few hundred milliseconds. Stream AI Moderation samples the call, scores those samples with the same policy as chat, and acts on the participant while the call is still running.

Stream Moderation benchmarks from live traffic: about 35 ms for English text, 1,500+ text requests per second, and about 300 ms to score a frame.

In under two years, prediction markets went from a niche corner of the internet for political junkies to platforms with sportsbook-level engagement. Kalshi and Polymarket alone have hit millions of monthly active users. For most industries, that growth curve gets a headline and a victory lap. For anyone responsible for trust and safety, it's the exact scenario that keeps them up at night.

Most teams still think about bot detection as a security problem. CAPTCHAs, rate limiting, DDoS mitigation, browser fingerprinting, that's the domain of the security team, or the network layer above it. It's the gate traffic hits before it ever reaches the product. But the bots causing the most damage in 2026 aren't trying to take

Everyone talks about models. New architectures, larger parameter counts, faster inference, those tend to dominate the conversation. But if you're actually building AI systems (or evaluating vendors), you quickly realize something else matters more: The data. Not just how much of it you have, but where it comes from, how it's processed, and how it evolves