Manual pipelines blocked researchers for 24 hours per test. A self-service cloud framework now completes evaluations in 20 minutes.
A speech AI company scaling its training data from 1 million to 12.5 million hours of audio to build complex speech understanding models.
Scaling to next-generation models required processing 10 times more data, but on-premises infrastructure bottlenecks meant evaluations took 24 hours....
“Managing the data processing pipelines and orchestration frameworks for training our new Universal models was overwhelming, and we didn't have the sufficient compute cluster or data storage capacity to support the volume and diversity of research we were doing. Our ability to innovate was stifled not by a lack of ideas, but by a lack of computing power and efficiency.”
AI models for speech-to-text transcription and audio intelligence.
Cloud computing services, AI infrastructure, and data analytics platforms for enterprises.
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Engineers spent 90% of time on data prep. New pipelines flipped that to 90% modeling and cut tuning from 7 days to 1 hour.
Engineers spent weeks manually configuring infrastructure. Now, they deploy pre-optimized models in minutes.
Users hung up on lagging voice agents. Speculative decoding on NVIDIA B200s cut latency from seconds to <400ms.
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.