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.
Summarizing answers took 14 seconds. Small language models cut that to 4 seconds and reduced costs by 4.2x.
Semantic searches took hours, blocking real-time tools. Now, partitioned data lets 16,000 apps query files instantly.
Engineers manually correlated alerts across systems. AI agents now diagnose issues and suggest fixes, cutting recovery time by 35%.
Minor edits required days of crew coordination. Now, staff use avatars to modify dialogue and translate languages instantly.
Lab supply orders were handwritten in notebooks. Digital ordering now takes seconds, saving 30,000 hours for research annually.
Experts spent 15 minutes pulling data from scattered systems. Natural language prompts now generate detailed reports instantly.