Data engineering has always been the invisible part of the data world. Not the dashboards, not the AI demos — the infrastructure underneath that determines whether any of it actually works.
Saranya built that infrastructure before cloud was the default, before data lakes were standard, before anyone called it a modern data stack. British Telecom. LatentView. Fortune 500 systems processing billions of records, the hard way, before the tooling made it easier.
The lesson was always the same: failures came from data that wasn't structured correctly. Systems that didn't understand the relationships between the things that actually mattered to the business. Data engineering isn't a technical detail. It's the act of teaching a system what your business actually is.
Now AI agents have arrived. And that lesson has become urgent for businesses. Agents are nothing like human analysts. They need explicit, unambiguous definitions of entities, relationships, and metrics. Without that, they don't flag uncertainty — they confidently do the wrong thing. At scale.
Deep data engineering, combined with genuine understanding of data science, statistical methods, and machine learning. They don't just build pipelines — they understand what data needs to look like for AI to reason about a business correctly.
Cuedo has been doing this work since before most of today's tools existed. That's the problem Cuedo exists to solve.