Scientists everywhere are working on solutions to a wide variety of problems, and to do that more efficiently, they want to ask more questions of their data and use their data in new ways. However, scientific data is complex, and organizing it on a large scale is not a trivial task. Furthermore, the scientists themselves often end up doing at least some of the data organization work even though it is not what they are trained in and generally not what they are interested in.
Modelyst implements data pipelines for R&D teams so that their scientific data can be used as effectively as possible. Simply put, Modelyst does the dirty data organization work so that scientists can spend all of their time on what they are experts in. It isn’t glamorous work – it’s just useful, and it doesn’t need to be oversold.
Being a standalone, very early stage startup, Modelyst had a few initial clients but needed a few more clients to get on their feet. They stood at a juncture where one failed contract could’ve meant the end of the company. The risk had started to stress out some founding members who had important life decisions to make and more personal responsibilities on the horizon.
Financial Resiliency through a pre-approved loan
Impact: Took away the pressure of immediate cash gains for sustenance
The co-founders were able to plan on a stable salary backed not only through their company’s revenue but also by a pre-approved loan from a pool of money that all Echelon entities contribute to.
Furthermore, it has allowed (and will continue to allow) the startup to stay alive long enough for it to seize its opportunity to grow, whenever that opportunity comes.