> ## Documentation Index
> Fetch the complete documentation index at: https://safety.ourdream.ai/llms.txt
> Use this file to discover all available pages before exploring further.
# How moderation works
> Defense in depth across input, output, behaviour, labelers, and reports.
ourdream's content moderation is built as
[**defense in depth**](https://en.wikipedia.org/wiki/Defense_in_depth_\(computing\)):
multiple independent layers stacked together, each tuned to catch a
different category of content. Each layer specialises in a different
class of signal, and the layers reinforce each other.
## The layers
### 1. Input
Every prompt is classified before any model runs against it. Chat
messages, image prompts, and character submissions pass through a
set of classifiers covering content, behaviour, and metadata signals.
Inputs that match the strictest signals are blocked before the model
runs at all; other flagged inputs are routed to the next layer.
### 2. Output
Generated images go through a separate set of classifiers that looks
at the output independent of the prompt. This is an independent check
on what the model actually produces, covering the categories that map
to [Prohibited content](/policies/prohibited-content). Outputs that
fail are blocked from delivery and may trigger account level review.
### 3. Metadata and behaviour
The third layer reads patterns rather than content. It looks at signals
across many actions by the same user, or across many users producing
similar content. Examples include accounts that produce content the
other layers reject at unusually high rates, and prompts that share
structural features with previously-blocked ones.
Behaviour signals usually do not block content on their own. They raise
the priority of human review.
### 4. Human reviewers
Trained reviewers see what the classifiers flag. Reviewers can:
* Approve content (release it from review).
* Reject it with a reason that maps to a specific policy.
* Escalate ambiguous cases to a senior reviewer or to the trust team.
* Flag patterns that the classifier layers should learn from; edge
cases feed back into the rules and training data.
For public character submissions the review queue runs in the
hundreds per day. For chat-generated content the volume is much
higher; the classifier layers handle the bulk of it, and the cases
that warrant a human reach a human.
### 5. Community reports
The last layer is everyone using the product. Users can
[report](/reporting/how-to-report) characters, images, videos, and
scenarios. Reports trigger re-review against the *current* policy,
which may be stricter than the one in force when the content was first
generated.
The most severe categories (underage content) are reviewed first.
## Why this shape
Each layer specialises. Classifiers handle volume at speed. Metadata
signals catch behavioural patterns. Human reviewers handle ambiguity
and judgement. The community surfaces what's actually playing out in
practice. Together they cover more ground than any single layer could
alone.
## The moderation team
Reviewers are vetted ourdream staff and trusted long-term community
contributors who have been onboarded into the moderation rota. They
operate under written guidelines and undergo regular calibration with
each other and with the trust team to keep judgement consistent.
Moderation decisions are made independent of growth and revenue
metrics. The trust team owns the policy; product owns the product.
We care about moderator wellbeing because the work is hard, and do
not tolerate abuse or harassment of our moderation team. The team
also meets regularly to review edge cases and update the guidelines.
## What gets rejected most often
In the public-submission queue, in rough order of frequency:
1. Profile images with underage cues.
2. Characters too close to existing IP.
3. Scenarios that frame non-consent as desirable — whether the user
is cast as perpetrator or the framing otherwise presents
non-consent as the appeal.
4. Names or descriptions matching the real-person blocklist.
Each of these is described on
[Prohibited content](/policies/prohibited-content).
## What classifiers see vs what humans see
Automated classifiers process **all** content on the platform —
including private chat messages and private generations — because
they have to in order to enforce the policies above. Classifier
processing happens in-line and no human reads the content as part of
it.
**Human reviewers** only see content that has been (a) flagged by a
classifier, (b) reported by a user, or (c) escalated as part of a
trust-team investigation. They do not browse private chats. Account
data (email, payment information, IP address) is gated behind trust-
team access and is touched only when an investigation requires it.
## Appeals
If a creator believes a rejection or removal was applied incorrectly,
the [Appeals](/moderation/appeals) process is the way to raise it. A
different reviewer handles the appeal, with the original objection in
mind. Appeals also feed back into the rules and training data, so the
work creators put into making the case rarely goes nowhere.