Monthly outages disrupted discovery. Event-driven scaling now lets scientists load multi-terabyte protein datasets directly into memory.
A biotech innovator analyzes millions of protein structures to design mini-proteins, aiming to replace serendipitous trial-and-error with engineered precision.
Reliability issues with a previous cloud provider caused monthly outages that disrupted critical research projects. The existing infrastructure could...
“Using AI to make intelligent choices in the discovery process helps us avoid scaling problems that traditional labs run into. Our major advantage with an AI, cloud-first approach is that we’re able to traverse a massive design space in a more methodical way.”
Generative AI platform for designing de novo mini-proteins for drug discovery.
Cloud computing services, AI infrastructure, and data analytics platforms for enterprises.
Provided hands-on engineering support to migrate Ordaōs' workloads to Google Cloud.
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R&D relied on costly "spray and pray" testing. Now, AI models simulate 40 billion molecules, cutting development time by 50%.
Predicting a single protein binding took 2,000 hours. Deep learning models now complete the task in just 15 minutes.
Antibody design relied on slow physical testing. With 12x faster AI predictions, researchers cut 1-2 wet-lab cycles per iteration.
Silos forced teams to manually chase data. Now, AI agents identify adverse events in seconds, eliminating weeks of cross-referencing.
Attorneys juggled fragmented tools to search 50,000 cases. An AI orchestrator now routes queries to sub-agents in a single chat.
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.