01 CONTENT CHANGE
Post, comment, or article
A member creates or edits meaningful community content.
Mylimb — AI-assisted content moderation
AI as a quiet first-pass safety layer for a community platform.
Boldcraft helped Mylimb check new and edited posts, comments, magazine articles, and images in the background. The AI identifies possible risk, creates reports when needed, and keeps final decisions with human moderators.
pending
Waiting for review
approved
No issue found
flagged
Report created
error
AI call failed
superseded
Newer edit exists
The problem being addressed
Mylimb is built around community participation. Members can create posts, comment in discussions, and publish longer magazine-style content. As that activity grows, relying only on manual review or member reports creates gaps: risky content may be noticed late, while moderators still spend time screening items that are likely safe.
The strategy
The moderation system treats AI as triage, not authority. A reusable Rails concern lets posts, comments, and articles opt into moderation. When meaningful fields change and AI moderation is enabled for the account, background jobs create a moderation record and call OpenAI moderation through RubyLLM.
01 — Where AI earns its place
AI is used only as a first-pass safety checker. When a member creates or edits meaningful content, Mylimb can review it in the background without forcing the user to wait.
02 — What gets reviewed
Posts send title, body text, and attachments. Comments send their text. Magazine articles send title, summary, article body, and cover image. Each model defines what matters, instead of duplicating moderation logic.
03 — How flagged content moves
Safe content is marked approved. Potentially unsafe content becomes a system-generated moderation report linked to the AI record, so it enters the same human review workflow as user-submitted reports.
04 — Why the record matters
Every pass stores what text and images were checked, what the AI returned, whether anything was flagged, and when it happened. If the content changes again, older checks are superseded so moderators work from the latest version.
AI should make the moderation queue smarter, not make sensitive decisions invisible.
Technical architecture
The implementation is a small moderation pipeline: content changes trigger review, the slow work happens in the background, every AI check becomes a record, and risky results enter the existing human report workflow.
02 TRIGGER
Moderation eligibility
The system checks whether AI review is enabled and whether important content changed.
03 BACKGROUND REVIEW
Queued safety check
The review runs outside the user-facing publishing flow.
04 AI CHECK
Text and image moderation
Prepared content is checked for possible safety risks.
SAFE Moderation record → approved
The item continues without moderator interruption.
FLAGGED System report → human review
The system creates a report linked to the AI record.
STALE Older record → superseded
Newer edits take priority over outdated checks.
Outcome
Boldcraft helped Mylimb add AI moderation as a background safety layer for community content. The system only runs when meaningful post, comment, article, or image content changes; sends the prepared content to OpenAI moderation through RubyLLM; stores an audit record; approves safe content; creates reports for flagged content; handles stale checks; and keeps final decisions with human moderators.