Reasons for this include:
Redundancy
Many experiments are unnecessary duplicates due to a lack of visibility into prior experiments.
Unusable results
Other experiments may fail to produce useful data, for example, if a suboptimal antibody fails to detect the presence of a protein.
Irreproducibility
Even when an experiment seemingly succeeds, scientists are often unable to replicate the results due to various factors.
It isn’t scientists’ fault this happens; they simply don’t have reliable access to the information that would enable them to prevent it. That’s where BenchSci comes in.
Curate the world's largest collection of data from life science experiments and reagent catalogs
Decode and organize the information with reagent-specific machine learning models
Provide rapid insights in a clear, easy-to-use interface with turnkey deployment across an enterprise
16 top 20 pharma companies trust BenchSci, along with over 50,000 scientists working at more than 4,500 institutions worldwide
Companies using BenchSci save millions per year in hard costs alone**
** Based on proprietary customer spend analysis. Depends on a number of factors, including a company’s total annual reagent and model system spend and its waste ratio.
Scientists using BenchSci move faster, with less time searching for and validating reagents and model systems
Over 80% of scientists say that BenchSci accelerates their work^
^ On average, from surveys conducted on hundreds of industry scientists across various therapeutic areas and geographies.In partnership with the world's leading scientific publishers
With proud support and funding from F-Prime, Gradient Ventures (Google’s AI fund), Inovia Capital, and TCV
