Using Riffyn Nexus together with JMP and other "lab of the future" tools, Imperial College London graduate student Michael Crone and his PhD advisor Paul Freemont were able to develop, validate, and roll out a reagent and plastics-agnostic COVID-19 diagnostic assay in only 9 weeks.
In 2017, Novozymes implemented Riffyn SDE to enable advanced lab automation, custom data processing and a revamped analytics pipeline in support of their advanced biofuels yeast product portfolio. This digital R&D infrastructure enhancement resulted in transformative workflows.
A global chemical producer used Riffyn Nexus to reinvent the entire data management system for their white biotechnology division and transform their R&D processes. Here is their story.
“It used to take a scientist 40 hours to manage three weeks of experiment data. With Riffyn Nexus it can now be done in one or two hours.” — Riffyn customer
What would 2400 hours over the course of a year buy you for your R&D processes? This is exactly the amount of time saved by a four-person team at a global pharmaceutical company when they used Riffyn Nexus to streamline their bioassay process. Here’s how they did it — and how your own teams can save time using Riffyn Nexus.
Using Riffyn Nexus, a global oral solid dose manufacturer was able to connect data across process steps in a matter of minutes instead of the 16 hours of error-prone effort it previously required. This allowed on-demand investigations of cause-and-effect that delivered significant new process insights.
Using Riffyn Nexus, an animal nutrition company was able to reduce the number of animals used in experimental trials by 80%, not only a significant savings in animal husbandry labor and feed, but also the elimination of unnecessary use of animals. Here’s how they did it.
Contextualizing and connecting data across formulations and analytical processes saved scientists 16 hours of time per experiment and delivered novel insights into their process design spaceSchedule a call
Increase in R&D productivity
Reduction in R&D personnel effort
Faster time to market for new products
Reduction in data preparation time