Millions of AI assets monthly demanded enterprise moderation. Alibaba Cloud's stack delivered 99.5%+ accuracy, no custom model needed.
A US-based startup offering students, researchers, and creators an all-in-one platform for AI chat, image generation, and video creation, producing millions of AI-generated assets monthly.
Delivering image and video generation required massive computational power with low-latency performance at this scale. Preventing harmful outputs...
“Alibaba Cloud has helped us to rapidly acquire market users while effectively mitigating risks through their robust AIGC services, large language models, and generative AI security capabilities.”
All-in-one AI assistant for study, work, and creativity with various generative tools.
Cloud computing and artificial intelligence platform for global enterprises.
EaseMate's Content moderation is part of this use case:
Related implementations across industries and use cases
Moderation couldn't keep pace with 600M users. AI agents now filter toxicity while models recognize 2.5B objects to refine search.
Writes and queries fought for the same nodes—safety latency spiked when uploads surged. Milvus separates them, stable at 10B+ vectors.
Elasticsearch treated creator vectors like log data, aging 400M+ to frozen storage, pushing search to 25s. A vector-native store fixed it.
Manual review slowed a viral Vogue contest. AI took over to moderate 56,000 entries, saving two weeks of editorial effort.
Generic tools misread student nuance, triggering false alarms. Now, an AI judge evaluates context, alerting teachers only to true crises.
Thousands of tables tracking the same metrics differently. No one knew which to trust — not humans, not AI. One governed layer changed that.
SThree people, three different AI answers. Grounded data sources fixed that — finance now has years of vendor spend at its fingertips.
Tournaments running simultaneously meant an hour of manual checks each. AI agents now run them in minutes, freeing the team to be proactive.
Large AI training jobs meant fighting for preemptible slots or leaving campus. Marlowe gave any lab guaranteed multi-node access on demand.
Millions of AI assets monthly demanded enterprise moderation. Alibaba Cloud's stack delivered 99.5%+ accuracy, no custom model needed.
A US-based startup offering students, researchers, and creators an all-in-one platform for AI chat, image generation, and video creation, producing millions of AI-generated assets monthly.
Delivering image and video generation required massive computational power with low-latency performance at this scale. Preventing harmful outputs...
“Alibaba Cloud has helped us to rapidly acquire market users while effectively mitigating risks through their robust AIGC services, large language models, and generative AI security capabilities.”
All-in-one AI assistant for study, work, and creativity with various generative tools.
Cloud computing and artificial intelligence platform for global enterprises.
EaseMate's Content moderation is part of this use case:
Related implementations across industries and use cases
Moderation couldn't keep pace with 600M users. AI agents now filter toxicity while models recognize 2.5B objects to refine search.
Writes and queries fought for the same nodes—safety latency spiked when uploads surged. Milvus separates them, stable at 10B+ vectors.
Elasticsearch treated creator vectors like log data, aging 400M+ to frozen storage, pushing search to 25s. A vector-native store fixed it.
Manual review slowed a viral Vogue contest. AI took over to moderate 56,000 entries, saving two weeks of editorial effort.
Generic tools misread student nuance, triggering false alarms. Now, an AI judge evaluates context, alerting teachers only to true crises.
Thousands of tables tracking the same metrics differently. No one knew which to trust — not humans, not AI. One governed layer changed that.
SThree people, three different AI answers. Grounded data sources fixed that — finance now has years of vendor spend at its fingertips.
Tournaments running simultaneously meant an hour of manual checks each. AI agents now run them in minutes, freeing the team to be proactive.
Large AI training jobs meant fighting for preemptible slots or leaving campus. Marlowe gave any lab guaranteed multi-node access on demand.