A community keeps its own hours and never sleeps. Your moderators do sleep, which is why the post that does real damage is the one that landed overnight.
Community app AI automation means every post is read the moment it lands rather than whenever a moderator gets to it. A model scores it against the guidelines you wrote. Everything doubtful queues for a person, because a wrong removal costs more trust than a slow one.
A platform where people talk grows in two directions. More members means more posts, and more posts means more of the few that should never have gone up. The team reading them does not grow at that rate.
Reports arrive faster than they are read, so the backlog is the normal state rather than a bad week.
A feed ordered by recency buries the post worth reading under twelve that are not.
A camera and a microphone bring consent and storage questions that a text feed never raised.
A model scores each post against your own community guidelines. The clear ones publish at once and the rest wait for a moderator to read them.
One model keeps the feed safe and orders it. Posts surface on how members actually respond rather than on the minute they were written.
Live rooms and uploaded clips get transcribed, so spoken content sits under the same rules as text. What a model cannot hear clearly goes to a person.
Four steps. A moderator sits at the last one, and the queue they read is short by design.
A member posts something to the feed.
A model scores it against your guidelines.
Clear posts publish. The doubtful ones wait.
A moderator reads the queue and decides.
Human checkpointThe same reading and scoring turn up in jobs that look unrelated until you notice each one is somebody judging a post.
Three answers a platform owner needs before anything else. Each one is something we decide rather than something we ask you to take on trust.
Content people put on your platform never trains a model. Where a hosted endpoint is used, retention gets switched off and the setting is checked on the account rather than assumed.
Reviews reject a declaration that does not match the app. The form is written from the code rather than from memory, and every permission the build requests is listed before submission.
Camera and microphone permission is requested at the moment it is needed, with the reason on screen. That is what GDPR asks for and it is also what stops a member reporting your app.
These are the pieces a community platform tends to need. Nothing here is a requirement. Your own stack is where the work starts.
Good on the obvious and unreliable on the rest, which is why the borderline calls queue rather than resolve. Sarcasm and in-jokes are where a general model fails. The fix is tuning on your own moderation history, and it is worth measuring on your posts before anybody commits.
It can, and on most platforms it should not. Automatic removal is right for the unambiguous cases and wrong everywhere else, because a wrong removal costs more trust than a slow one. Where that line sits is your decision rather than ours.
They are your guidelines rather than a general safety policy. Each rule you have written becomes something the model is scored against, and the ones it fails in testing get rewritten. A rule your moderators apply inconsistently will not work here.
No. The moderation and ranking sit behind your existing app as services it calls, so the product members use stays the product they know. Where an app was never built to expose its feed logic, week one establishes what that costs.
It removes the reasons a build can cause, and it does nothing about the rest. A declaration that matches the build and permissions asked at the right moment are what most rejections are actually about. No supplier controls what a reviewer decides on the day.
Cost lands per post rather than per month. A small open weight model on your own hardware handles the volume cheaply, and the larger model gets called only on posts it cannot settle. That split is where the running cost is decided.
Data entry, answering the same tickets, chasing numbers between systems. We automate the parts that repeat. Your team keeps the parts that need judgement.
