Protect your video streaming platform with real-time AI moderation that analyzes live streams, VOD content, thumbnails, and live chat at scale. Detect NSFW material, violence, copyright violations, and harmful content frame-by-frame before it reaches your audience.
From live streams to VOD libraries, our AI-driven moderation covers every aspect of video content safety with real-time detection and automated enforcement.
Monitor live video streams in real time with frame-by-frame AI analysis that detects NSFW content, violence, dangerous activities, and policy violations as they happen, enabling instant intervention before harmful content reaches viewers.
Automatically scan uploaded video-on-demand content through comprehensive frame analysis pipelines that identify inappropriate material, apply content ratings, and generate timestamp-level reports before videos are published to your catalog.
Industry-leading nudity and sexual content detection analyzes video frames with over 99% accuracy, catching explicit material in live streams and uploads even when partially obscured, overlaid, or briefly flashed on screen.
Advanced audio and visual fingerprinting technology detects copyrighted music, movie clips, TV show excerpts, and other protected content within streams and uploads, helping platforms comply with DMCA and global copyright regulations.
Filter toxic messages, spam, hate speech, and harassment in live stream chat at massive scale, processing thousands of messages per second with context-aware language models that understand streaming culture and community dynamics.
Ensure advertiser brand safety with granular content categorization that classifies video streams and VOD content across dozens of IAB categories, preventing ad placement alongside inappropriate or brand-damaging material.
Our AI engine processes live video streams frame by frame with sub-200ms latency, detecting policy violations the instant they appear on screen. From NSFW content flashing during a broadcast to violence breaking out on a live feed, our system identifies threats and triggers automated responses before harmful content spreads to your audience.
Every video uploaded to your platform passes through our multi-stage analysis pipeline that extracts key frames, runs parallel detection models for different content categories, and produces comprehensive safety reports with frame-level timestamps, confidence scores, and actionable moderation decisions.
Our infrastructure supports the world's largest video streaming platforms, processing millions of hours of content every day with consistent accuracy and low latency.
Real-time breakdown of detected content categories across monitored video streams and uploads.
Video streaming platforms face content moderation challenges of extraordinary complexity and scale. Unlike static text or image content, video is a continuous medium that combines visual imagery, audio, text overlays, and interactive elements into a temporal experience that must be analyzed across thousands or millions of individual frames. A single hour of high-definition video contains over 100,000 frames, each of which could contain policy-violating content ranging from nudity and graphic violence to copyrighted material and dangerous activities. When multiplied across the millions of hours of video uploaded and streamed on modern platforms every day, the scale of the moderation challenge becomes staggering.
Live streaming compounds these challenges by removing the buffer of pre-publication review. When a creator goes live, their video feed reaches viewers in near real-time, meaning that any policy-violating content, whether accidental, intentional, or the result of external interference, is broadcast to the audience before traditional moderation workflows can intervene. This reality demands moderation systems capable of analyzing video content with minimal latency and triggering automated responses within fractions of a second, all while maintaining the accuracy needed to avoid disrupting legitimate broadcasts.
The diversity of content on modern streaming platforms adds another layer of complexity. A single platform may host everything from children's educational content and cooking tutorials to mature gaming streams and live news coverage of violent events. Each content category requires different moderation standards, and the context in which potentially sensitive content appears, whether a documentary about war, a news broadcast, a medical education video, or an entertainment stream, fundamentally changes whether that content is appropriate for the platform. Effective moderation must understand this context to make intelligent decisions rather than applying blunt filters that suppress legitimate content alongside genuinely harmful material.
Our live stream moderation system operates by extracting frames from incoming video streams at configurable intervals, typically between two and ten frames per second depending on the risk profile of the stream and the platform's latency requirements. Each extracted frame is processed through a battery of specialized detection models running in parallel, with results aggregated and evaluated against the platform's content policies within milliseconds. This architecture enables the system to detect policy violations as they occur and trigger automated responses, from issuing warnings to the streamer to temporarily pausing or terminating the broadcast, before the offending content has been visible for more than a few seconds.
The frame extraction rate adapts dynamically based on real-time risk assessment. Streams from verified, high-reputation creators with clean moderation histories may be analyzed at lower frame rates to conserve computational resources, while new accounts, streams flagged by viewer reports, or streams in categories historically associated with higher violation rates receive more intensive monitoring. This adaptive approach allows platforms to allocate moderation resources efficiently while maintaining robust coverage across their entire streaming ecosystem, processing hundreds of thousands of concurrent live streams without performance degradation.
Detecting NSFW content in live video streams presents unique challenges beyond those encountered in static image analysis. Streamers may intentionally or accidentally expose explicit content for brief moments, relying on the transient nature of video to evade detection. Our models are trained to detect nudity and sexual content across a wide range of conditions including partial exposure, obscured or filtered imagery, content reflected in mirrors or displayed on screens within the stream, and rapid flashing of explicit material intended to appear for only a fraction of a second. The system maintains temporal awareness across consecutive frames, enabling detection even when explicit content appears for fewer than one second.
Beyond outright nudity, our detection models classify a spectrum of sexually suggestive content that may violate platform policies or require age-restricted viewing. This includes provocative poses, sexually themed activities, and content that uses overlays, costumes, or camera angles to create sexually charged presentations while remaining technically clothed. Platforms can configure their sensitivity thresholds to align with their community standards, choosing whether to flag only explicit nudity or to also capture suggestive content that may be inappropriate for general audiences or that violates advertiser brand safety requirements.
Violence detection in streaming video requires the ability to distinguish between depicted violence in games, movies, and fictional content and real-world violence captured in live broadcasts. Our violence detection models are trained on datasets that include both real and fictional violence across diverse contexts, enabling them to assess not only whether violence is present but whether it represents a genuine safety concern. A streamer playing a first-person shooter game generates violent visual content that is expected and contextually appropriate, while a live stream capturing a physical altercation, self-harm, or dangerous stunts represents a fundamentally different safety risk requiring immediate intervention.
The system also detects dangerous activities that may not constitute traditional violence but pose safety risks, including drug use, weapons handling, reckless driving filmed from within vehicles, and challenges or stunts that could inspire harmful imitation. These detections are particularly important for platforms where younger viewers may be present and where the culture of content creation sometimes incentivizes increasingly risky behavior for viewer engagement. Automated content warnings and age restrictions can be applied to streams containing these elements, and in cases of imminent danger, the system can alert platform safety teams for human intervention.
Video-on-demand content uploaded to streaming platforms passes through our comprehensive analysis pipeline before publication. The pipeline begins with intelligent keyframe extraction that identifies the most informative frames in the video, focusing computational resources on scene changes, high-motion segments, and other moments most likely to contain policy-relevant content. This approach reduces processing costs by up to eighty percent compared to uniform frame sampling while maintaining detection accuracy above ninety-nine percent, as our extraction algorithms prioritize the frames most likely to contain violations.
Each extracted keyframe is processed through multiple detection models simultaneously, covering content categories including nudity, violence, gore, weapons, drugs, self-harm, child safety, and copyright. Audio analysis runs in parallel, detecting copyrighted music, hate speech, and harmful verbal content. The pipeline produces a comprehensive moderation report with frame-level timestamps, confidence scores for each detected category, and recommended actions ranging from approval to age restriction to rejection. Platform moderators can review flagged content using our timeline interface that highlights specific moments requiring human judgment, dramatically reducing review time compared to watching entire videos.
Thumbnails and preview images represent the most visible element of video content on streaming platforms, appearing in search results, recommendation feeds, and browse pages where they are viewed by far more people than the underlying video. Inappropriate thumbnails, including clickbait images containing nudity, violence, or misleading content, can violate platform policies and damage user trust even when the video itself is compliant. Our thumbnail screening system analyzes both auto-generated thumbnails and custom uploads, ensuring that the visual representation of every video meets community guidelines before appearing in discovery interfaces.
The thumbnail analysis extends to clip and highlight moderation, where short segments extracted from longer videos are shared across social media and embedded on external sites. These clips often capture the most sensational moments of a stream or video, making them disproportionately likely to contain policy-violating content. Our system automatically screens clips and highlights before they can be shared, applying the same detection models used for full video analysis to these shorter content formats. This ensures that policy violations cannot escape the platform through excerpt sharing, maintaining brand safety across all distribution channels.
Live stream chat moderation operates at extreme scale, with popular streams generating thousands of messages per second during peak engagement moments. Our chat moderation system processes this message volume with sub-fifty-millisecond latency, analyzing each message for toxic content, hate speech, harassment, spam, and other policy violations before it appears in the chat interface. The system understands streaming-specific language patterns, emotes, copypastas, and cultural references that are commonplace in live chat environments, reducing false positives that would otherwise disrupt authentic community interaction.
Context-aware chat analysis goes beyond individual message filtering to detect coordinated harassment campaigns, chat raids, and organized brigading where groups of users target streamers or other chat participants with orchestrated abuse. The system identifies abnormal patterns in message timing, content similarity, and account creation dates that indicate coordinated attacks, enabling automated countermeasures such as follower-only mode, slow mode, or temporary chat restrictions that protect streamers without requiring manual intervention. Integration with viewer reporting systems provides additional signals that complement automated detection, creating a layered defense against chat-based harassment.
As video streaming platforms serve global audiences, content increasingly features subtitles, closed captions, and translated text overlays that must be moderated alongside visual and audio content. Our multi-language screening system analyzes embedded subtitles and auto-generated captions across over one hundred languages, detecting hate speech, harassment, misinformation, and other policy violations that may appear in text form within video content. This is particularly important for user-generated subtitles and community translations, which can be weaponized to insert harmful content into otherwise compliant videos without the creator's knowledge.
The subtitle screening system also supports compliance with regional content regulations that require specific warnings, disclaimers, or content modifications for different geographic markets. Content that is acceptable in one jurisdiction may require age restrictions, content warnings, or distribution limitations in another, and our system applies market-specific rules to subtitle and caption content to ensure global regulatory compliance without requiring separate content versions for each market.
Music copyright represents one of the most significant legal and financial risks for video streaming platforms. Our audio fingerprinting technology analyzes the audio track of every video and live stream, comparing it against a reference database containing millions of copyrighted recordings to identify licensed and unlicensed music usage. The system detects music even when it has been pitch-shifted, tempo-adjusted, overlaid with commentary, or mixed with other audio sources, maintaining detection accuracy above ninety-seven percent across diverse audio conditions. For live streams, copyright detection operates in real-time, enabling platforms to mute copyrighted audio segments or notify streamers before they accumulate DMCA violations.
Beyond music, our visual fingerprinting system identifies copyrighted video content including movie clips, TV show excerpts, sports broadcasts, and other protected visual media. The system creates perceptual hashes of reference content and compares them against uploaded and streamed video in real-time, detecting rebroadcast content even when it has been cropped, resized, mirrored, or subjected to color filters designed to evade detection. This comprehensive audio-visual copyright identification helps platforms comply with safe harbor provisions of the DMCA and equivalent international copyright frameworks while protecting the intellectual property rights of content owners who have registered their works.
Advertiser confidence is essential to the revenue model of ad-supported video streaming platforms, and a single high-profile incident of ad placement alongside harmful content can damage brand relationships worth millions of dollars. Our brand safety system classifies video content across granular IAB content taxonomy categories, providing advertisers with detailed information about the themes, topics, and sentiment of content where their ads may appear. This classification extends beyond simple safe-or-unsafe determinations to provide the nuanced categorization that sophisticated advertisers require for brand alignment.
The system operates at both the channel level and the individual video level, maintaining dynamic brand safety scores that update in real-time as new content is published and as viewer behavior patterns evolve. Advertisers can configure their placement preferences across detailed category specifications, excluding not only overtly harmful content but also content that may be safe for general audiences yet inappropriate for their specific brand positioning. This granular control enables platforms to maximize advertising revenue by matching brand-appropriate ads with suitable content while preventing the placement errors that erode advertiser trust and trigger ad spend reduction.
Content labeling and age-restriction systems help platforms meet regulatory requirements and user expectations around content transparency. Our automated labeling system analyzes video content across multiple dimensions, including violence level, sexual content, profanity, drug references, and thematic maturity, to generate appropriate content warnings and age ratings that are applied before viewers can access the content. These labels follow established rating frameworks used in the entertainment industry and can be customized to align with specific platform standards or regional regulatory requirements.
The labeling system integrates with age-restricted content gating mechanisms that verify viewer age before granting access to mature content. This is increasingly important as regulations in jurisdictions worldwide introduce stricter requirements for age verification on platforms hosting content unsuitable for minors. Our system supports multiple verification methods and applies geo-specific age-gating rules, ensuring that platforms maintain compliance across all markets they serve while providing a seamless user experience for viewers accessing age-appropriate content.
Streamers and video creators face unique safety challenges including doxxing, swatting threats, stalking, and targeted harassment campaigns that can escalate from online abuse to real-world danger. Our creator safety toolkit provides streamers with proactive protections including automated detection and redaction of personal information accidentally revealed during streams, such as addresses visible on packages, phone numbers on screens, or identifying information in browser tabs. The system also monitors chat and stream interactions for signals that indicate escalating threats, alerting both the creator and platform safety teams when intervention may be needed.
Viewer reporting integration creates a direct channel between audience members who witness concerning content or behavior and the platform's moderation infrastructure. Reports are triaged automatically based on severity, with urgent safety concerns escalated immediately while less critical reports enter standard review queues. The reporting system correlates viewer reports with automated detection signals, using the combination to identify situations that either system alone might miss. When multiple viewers independently report the same concern, the system elevates the priority and can trigger automated protective actions even before human reviewers assess the situation.
Our video moderation infrastructure is built on a distributed architecture designed to handle the massive processing demands of video content at scale. GPU-accelerated inference clusters deployed across global regions ensure low-latency processing regardless of where content originates, while intelligent load balancing distributes analysis workloads to maintain consistent performance during traffic spikes associated with major live events, platform promotions, or viral content moments. The system scales automatically from baseline capacity to handle traffic increases of ten times normal volume or more, ensuring that moderation quality never degrades due to infrastructure limitations.
Integration with existing streaming platform architectures is supported through multiple interface options including RESTful APIs for batch video analysis, WebSocket connections for live stream monitoring, webhook callbacks for asynchronous result delivery, and native SDKs for direct integration with popular streaming software and video processing pipelines. Comprehensive documentation, sandbox environments, and dedicated integration support teams ensure that platforms can deploy video moderation capabilities within days rather than months, with minimal disruption to existing content workflows and viewer experiences.
The clipping and highlight ecosystem that has grown around live streaming platforms creates unique moderation challenges. When viewers create clips from live streams, they tend to capture the most dramatic, controversial, or sensational moments, producing short-form content that is disproportionately likely to contain policy violations. These clips can be shared across social media, embedded on external sites, and compiled into highlight reels that reach audiences far beyond the original stream, making effective clip moderation essential for controlling content distribution and maintaining platform reputation.
Our clip moderation system integrates directly with platform clipping features, analyzing each clip through the same detection pipeline used for full video content before it can be shared or distributed. The system maintains context from the original stream, understanding that a clip of a violent scene from a video game may be acceptable while an identically framed clip of real-world violence requires immediate action. This contextual awareness extends to audio analysis, ensuring that clips containing copyrighted music, hate speech, or other audio violations are caught even when the visual content is compliant. The result is comprehensive clip moderation that maintains content safety across all distribution channels without adding friction to the authentic clip-sharing behavior that drives platform engagement and growth.
Live visualization of our alert processing system monitoring streams across the platform.
Everything you need to know about implementing content moderation for your video streaming platform.
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