Query spikes choked the old matcher. Zilliz Cloud cut latency to 50ms for 2M daily searches, enabling growth to 100k users.
An AI-native platform processes over 400,000 daily job posting updates and millions of user interactions requiring real-time responsiveness.
A Python-based matching system struggled with simultaneous data ingestion and query spikes, causing inconsistent response times of 200-500ms and...
“Choosing Zilliz Cloud was one of our best early decisions. It enabled us to build the product we envisioned rather than the product our infrastructure limitations would allow. In AI applications, that difference often determines success or failure.”
AI job search copilot for resume tailoring, job matching, and referral networking.
Vector database platform for building and scaling AI applications.
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Inconsistent data on 200M items broke keyword search. A hybrid vector engine now pairs visuals and text to find items despite missing tags.
Trainers manually analyzed scattered sleep and diet logs. AI now unifies the data to trigger instant coaching insights.
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.