01
Data quality is the ceiling. Models don't break through it.
Swap the model. Tune the prompt. Scale the infra. If the data is messy, the ceiling doesn't move. We've hit it enough times to stop waiting for a shortcut.
We're on a mission to empower teams that build with data to unlock the incredible potential of AI.
Across teams, the promise of data, and now AI, is constantly bottlenecked by the same underlying problem: turning messy, fragmented inputs into something structured, reliable, and usable.
Bo Lei built Netflix's data mesh—the kind of system that keeps real-time data flowing at global scale, maintained by huge teams on permanent call. At Splunk's UBA team, he saw a different version of the same problem: 80% of every sprint went to wrangling logs. The actual detection work—the part that mattered—couldn't get done.
Yichen Jin saw the same pattern from another angle. Running data infrastructure at a quant fund, she worked with hundreds of terabytes of financial signals—data that was effectively useless until it was normalized. In healthcare fraud detection, she saw how even something as basic as billing codes varied wildly across systems. The insights were there. But getting the data into shape to see them was the real work.
What we've learned the hard way.
02
Every era of data needed a new normalization layer. This is ours.
Batch processing needed one. Streaming needed one. AI agents need one that doesn't require engineers watching it around the clock.
03
Engineers shouldn't spend their careers babysitting parsers.
We watched it at Netflix, Splunk, hedge funds, hospital systems. The people were sharp. The pipelines consumed them anyway.
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