Over time, the rate of improvement in AI models has outpaced that of pipelines intended to support them. Teams are moving towards more complex signals and higher workloads, but it becomes increasingly difficult for the pipelines to support this. This gap widens with every new data source that adds to this stack, forcing the engineers to hold together workflows that were never designed to work together.

Performance slows, iteration decreases, and now the system begins to limit the very models it was built to support. This issue is solved by a unified data flow, which ensures that AI has a scalable structure. The sections below will break down the key details on why this change is so important.