Engineers spent 90% of time on data prep. New pipelines flipped that to 90% modeling and cut tuning from 7 days to 1 hour.
A generative AI company building advanced voice models and deepfake detection tools for banking and law enforcement, dealing with datasets that ballooned to over 60 terabytes.
Engineers spent 90% of their time on manual data preparation and file transfers rather than model building, leaving high-performance accelerators...
“Google Cloud was the natural fit to modernize infrastructure with AI-optimized tools and architecture.”
Generative AI platform for voice cloning, text-to-speech, and deepfake detection.
Cloud computing services, AI infrastructure, and data analytics platforms for enterprises.
Related implementations across industries and use cases
Manual pipelines blocked researchers for 24 hours per test. A self-service cloud framework now completes evaluations in 20 minutes.
Building an agent took 50 people weeks of work. A bank now designs and tests the same tool in just four days.
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.