Manual estimates risked $300k units not fitting. Teams now simulate layouts in a digital twin, verifying fit before physical install.
A biotechnology company aiming to map disease pathway signatures for all known diseases to accelerate drug discovery timelines from years to months.
Integrating lab automation units costing up to $300k requires precise spatial planning, but traditional manual estimation often led to layout errors...
“The digital twin allowed us to plan our lab expansion with confidence. In the past, we would have had to imagine how instruments might fit into the space, leaving little margin for error. Now, we can simulate and test different configurations before making changes, which helps us avoid costly mistakes.”
Transcripta Bio's Laboratory design simulation is part of this use case:
AI-driven drug discovery using transcriptomic atlases to predict biological responses.
NVIDIA is a technology company that specializes in semiconductors, graphics processing units, and artificial intelligence for applications in data centers, gaming, and more.
Developed a digital twin solution for Transcripta Bio to virtually design and validate lab layouts.
Related implementations across industries and use cases
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Matching phages required screening trillions of options. Gen AI simulates interactions, cutting discovery from 10 years to 2 months.
Data silos blocked real-world simulation. Now, teams validate site physics and safety risks on a unified digital twin.
Factory design was slow and siloed. Now, digital twins run thermal simulations 150x faster, validating global layouts in minutes.
Manual scheduling trapped recruiters in logistics. AI now automates interview coordination, freeing teams for strategic work.
Teams reactively managed trials across scattered systems. AI now integrates data to predict bottlenecks and recommend interventions.
Manual translation dragged 60-minute calls to 90+, silencing experts. Now, live AI captions let teams collaborate in native languages.
Fleet repair estimates took up to 48 hours. Autonomous agents now process them in 30 minutes, absorbing a 10x volume surge.
Manual estimates risked $300k units not fitting. Teams now simulate layouts in a digital twin, verifying fit before physical install.
A biotechnology company aiming to map disease pathway signatures for all known diseases to accelerate drug discovery timelines from years to months.
Integrating lab automation units costing up to $300k requires precise spatial planning, but traditional manual estimation often led to layout errors...
“The digital twin allowed us to plan our lab expansion with confidence. In the past, we would have had to imagine how instruments might fit into the space, leaving little margin for error. Now, we can simulate and test different configurations before making changes, which helps us avoid costly mistakes.”
Transcripta Bio's Laboratory design simulation is part of this use case:
AI-driven drug discovery using transcriptomic atlases to predict biological responses.
NVIDIA is a technology company that specializes in semiconductors, graphics processing units, and artificial intelligence for applications in data centers, gaming, and more.
Developed a digital twin solution for Transcripta Bio to virtually design and validate lab layouts.
Related implementations across industries and use cases
Physics simulations took weeks to screen libraries. Boltz-2 runs 1,000x faster, cutting this to hours with 2x precision.
Predicting a single protein binding took 2,000 hours. Deep learning models now complete the task in just 15 minutes.
Matching phages required screening trillions of options. Gen AI simulates interactions, cutting discovery from 10 years to 2 months.
Data silos blocked real-world simulation. Now, teams validate site physics and safety risks on a unified digital twin.
Factory design was slow and siloed. Now, digital twins run thermal simulations 150x faster, validating global layouts in minutes.
Manual scheduling trapped recruiters in logistics. AI now automates interview coordination, freeing teams for strategic work.
Teams reactively managed trials across scattered systems. AI now integrates data to predict bottlenecks and recommend interventions.
Manual translation dragged 60-minute calls to 90+, silencing experts. Now, live AI captions let teams collaborate in native languages.
Fleet repair estimates took up to 48 hours. Autonomous agents now process them in 30 minutes, absorbing a 10x volume surge.