Lab methods slowed protein analysis. An AI pipeline now scales test datasets from five items to one million in just two hours.
A biotech startup designing novel protein-based building blocks to replace traditional materials that account for 23% of global greenhouse gas emissions.
Developing sustainable alternatives requires analyzing complex protein structures at a massive scale. The company needed infrastructure capable of...
“Materials are everywhere around us. They’re also responsible for 23 percent of global greenhouse gas emissions.”
AI-driven molecular design and manufacturing platform for sustainable materials.
Cloud computing platform and on-demand infrastructure services.
Related implementations across industries and use cases
Antibody design relied on slow physical testing. With 12x faster AI predictions, researchers cut 1-2 wet-lab cycles per iteration.
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.
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.