Legacy tools choked on billions of records. A central vector store now unifies fragmented apps for 5x faster, context-aware search.
A productivity AI platform serves millions of users by processing billions of conversation events across disparate tools like Zoom, Slack, and Salesforce.
Rapid growth demanded near-real-time retrieval across billions of records, but existing vector databases lacked the multi-tenancy support required...
“We've got millions of monthly active users and all of the underlying data when we're trying to go find related conversations, find updates to an action item, find referenced documents...Milvus serves as the central repository and powers our information retrieval among billions of records.”
AI copilot for meeting summaries, transcripts, and enterprise search across workflows.
Vector database platform for building and scaling AI applications.
Related implementations across industries and use cases
Search slowed to 1000ms as transcripts hit 30M hours. Migrating the vector engine cut latency to 100ms—10x faster.
Daily updates delayed new content. A unified database now runs twice-daily refreshes with zero downtime.
Summarizing answers took 14 seconds. Small language models cut that to 4 seconds and reduced costs by 4.2x.
Daily updates delayed new content. A unified database now runs twice-daily refreshes with zero downtime.
Summarizing answers took 14 seconds. Small language models cut that to 4 seconds and reduced costs by 4.2x.
Moderation couldn't keep pace with 600M users. AI agents now filter toxicity while models recognize 2.5B objects to refine search.
Sellers lost hours to manual research. AI agents now prioritize leads and draft briefs, cutting prep time by 80%.
Analyzing 10TB of weekly telemetry took IT specialists days. Now, engineers ask AI in natural language to instantly retrieve charts.
Custom hardware bottlenecked imaging. A switch to software-defined GPUs now renders photorealistic 3D hearts in real time.
Legacy tools choked on billions of records. A central vector store now unifies fragmented apps for 5x faster, context-aware search.
A productivity AI platform serves millions of users by processing billions of conversation events across disparate tools like Zoom, Slack, and Salesforce.
Rapid growth demanded near-real-time retrieval across billions of records, but existing vector databases lacked the multi-tenancy support required...
“We've got millions of monthly active users and all of the underlying data when we're trying to go find related conversations, find updates to an action item, find referenced documents...Milvus serves as the central repository and powers our information retrieval among billions of records.”
AI copilot for meeting summaries, transcripts, and enterprise search across workflows.
Vector database platform for building and scaling AI applications.
Related implementations across industries and use cases
Search slowed to 1000ms as transcripts hit 30M hours. Migrating the vector engine cut latency to 100ms—10x faster.
Daily updates delayed new content. A unified database now runs twice-daily refreshes with zero downtime.
Summarizing answers took 14 seconds. Small language models cut that to 4 seconds and reduced costs by 4.2x.
Daily updates delayed new content. A unified database now runs twice-daily refreshes with zero downtime.
Summarizing answers took 14 seconds. Small language models cut that to 4 seconds and reduced costs by 4.2x.
Moderation couldn't keep pace with 600M users. AI agents now filter toxicity while models recognize 2.5B objects to refine search.
Sellers lost hours to manual research. AI agents now prioritize leads and draft briefs, cutting prep time by 80%.
Analyzing 10TB of weekly telemetry took IT specialists days. Now, engineers ask AI in natural language to instantly retrieve charts.
Custom hardware bottlenecked imaging. A switch to software-defined GPUs now renders photorealistic 3D hearts in real time.