Resources

A 1 MW data center does not draw 1 MW. It draws 1.474 megawatts at the utility meter, or 1.352, depending on how much of the cooling system you are willing to actually model.

LLMs get you 80% there in seconds. The last 20% is where the work lives. Here's why that gap is smaller to close in software and catastrophic to ignore in large capital projects.

In this Roebling use case we leveraged AI-proposed structural changes to model an ISS power distribution channel, then redeploying the same model to lunar and Mars conditions.

A static techno-economic analysis becomes stale the moment you stop editing it. Here's why a dynamic TEA has to be a living decision tool, not a deliverable.

A wrong assumption in FEL-1 is a few hours of rework. The same assumption in FEL-3 can kill the project. Here's why tracking uncertainty is the whole job, and where AI actually helps.

Explore 5 takeaways on tracking assumptions in an early stage TEA before they compound from a recent webinar with Crystal Bleecher and UC Berkeley’s Frederick Twigg.

In this on-demand session, Brentan Alexander share a new generation of AI-enabled integrated modeling tools.

Synonym Biotechnologies is becoming Roebling. After working with customers from startups to Fortune 500 companies as Synonym we are now Roebling.