We saw data engineering teams spending most of their time on work that buries them - investigating broken pipelines, tracing issues across systems, stitching context across tools that were never designed to talk to each other. No tool understood the full environment well enough to actually do the work.

So we built one.

Not because the AI wasn't good enough. Because nothing had packaged the environment in a way the AI could actually use. Solve that, and the day-to-day transforms. The engineer stops being the person who holds all the context in their head and starts being the person who decides what the data means, how it flows, and what the organization builds on top of it. The knowledge engineer.