This is like hiring millions of super-fast digital editors who watch everything posted on a social network in real time—hiding abusive or illegal content, flagging rule‑breaking posts, and deciding what to show in people’s feeds based on their interests.
Reduces the overwhelming manual effort and cost of reviewing user-generated content at scale, improves safety and brand integrity on the platform, and increases engagement by personalizing what each user sees.
Proprietary behavioral and interaction data at massive scale, continuously improving moderation and recommendation models, and deeply embedded AI in core product workflows that increase switching costs for both users and advertisers.
Hybrid
Vector Search
High (Custom Models/Infra)
Inference latency and cost at extreme scale, plus data privacy and regulatory constraints around automated moderation decisions.
Early Majority
Differentiation typically comes from the quality and speed of moderation, the sophistication of ranking algorithms for feeds and recommendations, and the breadth of multimodal understanding (text, image, video) rather than from the basic use of AI itself, which is now standard across major social platforms.