> 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-building-a-platform.md).

# Wave 3 – Building a Platform

Achieving Product Market Fit, to then Cost-Effectively Horizontalize and Build a Competitive Moat

W3 represents a move towards horizontal models that first focus on a killer app to **(1) achieve product market fit, (2) scale, and (3) cost-effectively extend features into new domains.** 

Horizontalizing can mean expanding within a large-scale facility, across multiple product domains or industries, or through integrating a new data source. In the case of physical robots, entering W3 often means refactoring the software used in W2 to realize user-friendly and rapidly deployable solutions.

The ROI focus shifts from a mechanically and economically feasible standalone solution to a synergistic hub of integrated solutions. W3 marks the move towards overall economics of the platform. As network effects kick in, value delivered to the customer increases in a non-linear fashion, translating into a higher revenue potential. On the other hand, tapping into economies of scale and scope make per unit cost fall, part of which translate into higher customer value.

Both the top-line and bottom-line effect are amplified as we build for commercial scale. This is where the true value to the customer is unlocked. **The market size multiplier of platform plays is why we find W3 so compelling.**

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