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PRODUCTS
If you’re running a platform that accepts user-generated video, you already know the uncomfortable truth: moderation does not fail gradually. It fails suddenly, loudly, and usually in production.
At small scale, manual review works. Someone watches uploads, flags edge cases, and moves on. But once videos start arriving continuously, from social feeds, marketplaces, learning platforms, or creator tools, that approach collapses. Review queues grow. Turnaround times stretch. False positives pile up. And worst of all, unsafe content slips through simply because no one saw it in time.
At that point, moderation stops being a policy discussion and turns into a workflow design problem. You need a system that can analyze video automatically, apply consistent rules, and only involve humans when confidence drops below your threshold.
That’s exactly what we’re going to build.
Video moderation breaks down when analysis and policy are tightly coupled. If the system that detects content also decides what happens next, even small policy changes turn into risky code changes. That’s where most moderation pipelines become hard to scale and harder to maintain.
FastPix and n8n solve this by splitting the problem into two clear layers.
FastPix is responsible for analysis. It ingests video, runs AI models on the audiovisual content, and returns structured confidence scores for categories like sexual content, violence, self-harm, and profanity. This work is compute-heavy and asynchronous by nature, so FastPix runs it independently and reports back when results are ready. At no point does FastPix decide whether a video should be blocked, reviewed, or approved. It only produces signals.
n8n is responsible for decision-making and orchestration. It receives those signals, applies your platform’s rules, and triggers the appropriate actions. Thresholds, review queues, notifications, access changes, and audit logging all live here. Because this logic is encoded as workflows instead of backend code, policies can evolve without redeploying services or retraining models.
Together, this creates a moderation system where video analysis and policy execution move at different speeds, for different reasons.
FastPix answers what the video contains.
n8n decides what your platform does about it.
Humans are involved only when confidence drops below acceptable thresholds. That separation is what keeps the system predictable, scalable, and easy to change as your platform grows.
A scalable video moderation system is fundamentally event-driven. Instead of reviewing videos synchronously at upload time, the system reacts to new content, analyzes it asynchronously, and applies policy decisions once enough information is available.
At a high level, the workflow looks like this:
What’s important here is not the number of steps, but where decisions happen.
Analysis happens once, produces structured signals, and completes independently of your application. Decision-making happens later, based on those signals and your platform’s evolving rules. This keeps uploads fast, avoids blocking user actions, and prevents moderation logic from leaking into core application code.
With n8n, this entire flow lives in a single, observable workflow. Webhooks trigger the pipeline, HTTP calls invoke FastPix, conditional nodes enforce policy, and integrations handle notifications or storage. There’s no need for custom workers or long-running background jobs the workflow itself becomes the moderation engine.
This architecture scales cleanly as volume increases, because each step is decoupled and can evolve independently.
Every moderation workflow starts with a simple trigger: a new video exists.
The mistake many teams make is treating moderation as something that must happen synchronously at upload time. That slows down user flows and couples moderation logic too tightly to your core application.
A better approach is to treat uploads as events, not blocking operations.
Your application’s responsibility ends once it signals that a video is ready. From there, the moderation pipeline runs independently.
In practice, there are a few common ways teams emit this signal:
In n8n, this is handled with a Webhook Trigger node. You expose an endpoint such as /video/uploaded that accepts a POST request containing the video URL, a video ID, and optional metadata like uploader ID or content type.
This webhook is an important boundary. It cleanly separates your product from the moderation system. Your app stays responsive, and moderation logic stays out of request paths that affect user experience.
At this stage, nothing is analyzed and nothing is decided. The system has simply acknowledged that a new video needs evaluation. That lightweight, event-driven start is what keeps the rest of the pipeline scalable.
Once n8n receives the upload event, the workflow moves from event detection to analysis. This is the point where the video is handed off to FastPix so AI-based moderation can run independently of your application.
FastPix’s moderation API is designed to work asynchronously. You don’t block the workflow while analysis runs, and you don’t embed moderation logic into your backend. Instead, you declare what analysis you want, submit the video, and wait for results via webhook.
At this stage, n8n’s job is simply to make a well-formed API request and pass along the video reference it received earlier.
In your n8n workflow, add an HTTP Request node configured to call FastPix’s on-demand API.
Use the following settings:
Endpoint
POST https://api.fastpix.io/v1/on-demandHeaders
Authorization is handled using your FastPix Token ID and Token Secret (via Basic auth), and the request body is sent as JSON.
Request body
{
"inputs": [
{
"type": "video",
"url": "{{$json.videoUrl}}"
}
],
"accessPolicy": "public",
"moderation": {
"type": "av"
}
}
This request tells FastPix to ingest the video from the provided URL and run audiovisual moderation as part of the processing job. The moderation.type field indicates that both visual and audio signals should be analyzed, which allows FastPix to detect things like NSFW content, violence, and profanity derived from speech.
The important thing to note here is that FastPix does not return moderation results immediately. Instead, it acknowledges the request and begins analysis asynchronously. This keeps your workflow responsive and avoids timeouts when videos are long or processing takes longer than expected.
Make sure the videoUrl value matches the key used in your webhook payload from Step 1. n8n’s expression syntax lets you map this dynamically without hardcoding values.
At this point, analysis is in progress, but no decisions have been made. The workflow has successfully handed the video off to the analysis layer and is now waiting for results.
Video moderation is not an instant operation. AI analysis takes time, especially when you’re processing longer videos or running multiple detection models. Polling an API to check status is possible, but it adds unnecessary complexity and load.
FastPix avoids this by using webhooks. Instead of your workflow asking, “Is the job done yet?”, FastPix notifies you when moderation is complete.
This event-driven approach keeps the pipeline efficient and predictable.
To receive moderation results, you first register a webhook endpoint that FastPix can call once analysis finishes.
In the FastPix dashboard or via API, configure a webhook callback URL pointing to your n8n instance, for example:
https://<your-n8n-domain>/webhook/video/moderation/ready Next, subscribe this endpoint to the video.mediaAI.moderation.ready event. This ensures FastPix sends a POST request to your workflow as soon as moderation results are available.
The webhook payload contains structured data, including moderation categories and confidence scores, which your workflow will use in the next step.
In n8n, add a second Webhook Trigger node to receive the callback from FastPix.
This node should be configured to accept POST requests at the same path you registered earlier. Its role is simple but critical: it marks the point where analysis ends and decision-making begins.
Once the webhook fires, n8n extracts the moderation results from the request payload and passes them downstream. At this stage, you have everything you need to apply policy rules no polling, no retries, and no guessing about job state.
By relying on webhooks instead of synchronous checks, the workflow remains responsive, resilient, and easy to scale as moderation volume increases.
Once n8n receives the moderation payload from FastPix, the system moves from analysis to decision-making. At this point, the video has already been classified. What remains is to determine how your platform should respond.
FastPix returns confidence scores between 0 and 1 for categories such as sexual content, violence, self-harm, and profanity derived from audio or speech. These scores are signals, not verdicts. They describe what the models detected, along with how confident the detection is.
In n8n, these signals are evaluated using If / Else or Switch nodes. This is where platform policy is applied.
A common pattern is to map confidence ranges to actions, for example:
These thresholds are not universal. They reflect your tolerance for risk, legal obligations, and community standards. The key advantage of using n8n is that these rules live outside your application code. Changing policy does not require redeploying services or retraining models. It’s a workflow edit.
At this stage, you can also persist moderation outcomes. Many teams store the raw confidence scores, applied labels, and final decision in a database. This makes moderation decisions auditable and helps with appeals, reporting, and long-term policy tuning.
By keeping this logic explicit and data-driven, moderation becomes predictable and explainable. Models generate signals. Workflows enforce rules. Humans step in only when the system is uncertain.
Once a moderation decision is made, the workflow executes the outcome across your platform. Typical actions include:
If a video crosses a high-risk threshold, update its access policy to prevent public playback. This ensures unsafe content is not streamable, even if a URL already exists.
For medium-confidence cases, route the video to a moderation queue. Send alerts via Slack, email, or your internal ticketing system with:
This gives reviewers the context they need without re-running analysis.
Low-risk videos can be automatically approved. Mark them as publishable and allow downstream workflows such as distribution or indexing to continue.
Persist moderation outcomes for audit and reporting:
This is essential for compliance, appeals, and policy tuning.
All actions should live in the workflow layer. Avoid hardcoding enforcement logic in your application so policies can evolve without redeployments.
A production n8n workflow for video moderation is intentionally simple. Each node has a single responsibility, and the flow mirrors the lifecycle of a video from upload to enforcement.
A typical workflow includes the following stages:
Under the hood, most moderation workflows rely on a small, repeatable set of node types:
This modular structure keeps the workflow easy to understand and easy to change. As moderation rules evolve, you adjust nodes and thresholds instead of rewriting backend services.
Below is a simplified n8n workflow JSON that captures the core structure of a video moderation pipeline. This is not a complete, production-ready workflow. Instead, it shows the minimum set of nodes needed to move a video from upload to moderation and into decision logic.
The goal here is to illustrate flow, not configuration details.
{
"nodes": [
{
"id": "1",
"type": "n8n-nodes-base.webhook",
"parameters": {
"httpMethod": "POST",
"path": "video/uploaded"
}
},
{
"id": "2",
"type": "n8n-nodes-base.httpRequest",
"parameters": {
"url": "https://api.fastpix.io/v1/on-demand",
"options": {
"headers": {
"Authorization": "Bearer <token>",
"Content-Type": "application/json"
}
},
"body": "{\"inputs\":[{\"type\":\"video\",\"url\":\"{{$json.videoUrl}}\"}],\"accessPolicy\":\"public\",\"moderation\":{\"type\":\"av\"}}"
}
},
{
"id": "3",
"type": "n8n-nodes-base.webhook",
"parameters": {
"httpMethod": "POST",
"path": "video/moderation/ready"
}
},
{
"id": "4",
"type": "n8n-nodes-base.function",
"parameters": {
"functionCode": "parse moderation"
}
}
]
}
At a high level, this workflow does four things.
First, it exposes a webhook endpoint that your application calls whenever a new video is uploaded. This is the entry point into the moderation pipeline and keeps uploads decoupled from moderation logic.
Second, it submits the video URL to FastPix using an HTTP Request node. This triggers asynchronous AI moderation without blocking the workflow or your application.
Third, it defines a second webhook endpoint that FastPix calls once moderation analysis is complete. This event-driven handoff replaces polling and marks the transition from analysis to decision-making.
Finally, it passes the moderation payload into a function or conditional node where scores are parsed and policy logic is applied. In a real workflow, this step typically branches into approval, review, or blocking paths.
In production, you would extend this with additional nodes for threshold evaluation, notifications, database writes, and access control updates. But the core pattern stays the same: event in, analysis out-of-band, decision via workflow.
We also have a content to on how to automate video clipping with n8n
Combining n8n’s workflow automation with FastPix’s video and AI APIs gives you a practical way to automate video moderation at scale, without hardcoding policies or blocking user workflows.
From a single API, you can:
Whether you’re moderating user-generated content, creator uploads, marketplace listings, or learning content, FastPix with n8n lets you treat moderation as infrastructure, not a manual process.
FastPix is available as SDKs in Node.js, Python, Go, PHP, and C#, so you can integrate moderation into whatever stack you already use. Sign up to try FastPix and get $25 in free credit.
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