> For the complete documentation index, see [llms.txt](https://bee-partners-1.gitbook.io/bee-the-machine-to-machine-learning-vector/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://bee-partners-1.gitbook.io/bee-the-machine-to-machine-learning-vector/wave-3/wave-3-opportunity-areas/accelerating-serendipitous-discovery.md).

# Accelerating Serendipitous Discovery

### **The Foundry Model**  

Foundries are integrated facilities with a defensible data moat that can *design, build, test and produce bio-products at scale*, with the vision to develop various products atop a horizontalized platform.

* Examples include [Ginkgo Bioworks](https://www.ginkgobioworks.com/), [Triple Bar Bio](https://www.triplebar.com/), [Infinome Biosciences](https://infinomebio.com/), and [WuXi AppTec](https://www.wuxiapptec.com/).

W3’s horizontalized platform reflects an end goal of flexible hardware (e.g. bioreactors) that enables plug and play of novel development techniques (e.g. strain innovation) to deliver novel products without any hardware changes.

Foundries are optimizing not just for time savings, but also to maximize margins and expedite compliance; this outcome is achieved by expertly enriching and mixing datasets.

What is truly powerful, is that enhancing design via computational discovery and abstracted insights across platform verticals, means not just a bottom line shrinkage, but massive top line growth.

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