How Roblox Uses AI to Moderate Content on a Massive Scale
Roblox's user-generated platform grew in both scale and speed far beyond what human moderators could handle alone, requiring scalable AI infrastructure to moderate billions of pieces of content in real time across dozens of languages.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · Content submitted by users
Users send chat messages, voice communications, and upload assets to the platform every day.
Tools used
transformer-based text filterPII filtervoice safety classifierlarge language models (LLMs)
Outcome
AI moderation now handles over 750,000 text filter RPS and 370,000 PII filter RPS at peak. The PII filter reduced false positives by 30% and increased automatic PII detection by 25%. The voice safety classifier achieves 92% higher recall than the initial version at a 1% false positive rate. Real-time feedback interventions reduced filtered chat messages by 5% and abuse-report consequences by 6%.
What failed first
An earlier rules-based text filter and CPU-based serving infrastructure could not keep pace with the volume and speed demands of the platform as it scaled.