Team
We make agent-assisted work visible, attributable, and improvable. We believe that you should know not only how many tokens your agent took to build a feature but what documentation it used to do it and how you can make it faster/cheaper/better next time.
We care deeply about providing what you need to do your job. Agents "care" (as far as that word can be stretched) about fulfilling the goal provided to them. We think squaring these worlds is an engaging engineering and human-factors challenge.
Observe what happened, trace the supporting evidence, make a change, and observe again. Feedback becomes useful when it informs the next decision.
People interpret the evidence and remain accountable for the result. Activity is not an outcome, and attribution alone does not establish improvement.
While we're building infrastructure for AI agents, our ultimate goal is to empower humans. We design our systems to augment human capabilities, not replace them.
An experienced technology leader and entrepreneur, Bryan co-founded Datalogue, an AI-enabled data platform trusted by major enterprises, which was acquired by Nike in 2021. He subsequently directed product and infrastructure efforts at Caurus, expanding it into a seven-figure operation. His expertise centers on connecting sophisticated infrastructure with measurable business results, assisting Fortune 500 organizations in resolving significant data challenges.
During his tenure at Google, Alex made contributions to core systems across Search, Knowledge Graph, and time series infrastructure at scale. He has also supported emerging XR technologies and maintains involvement in the Rust and open data sectors. His work with initiatives like DataFusion has been presented in industry fora. Alex demonstrates skill in converting complex technical challenges into practical implementations with substantial user benefits.
Ben is a systems engineer with a bias for building things the hard (and correct) way, owning problems end-to-end, from distributed runtimes to high-performance simulation tooling. He brings deep experience in Rust, with a focus on concurrency, async systems, and performance tradeoffs. Outside of core infrastructure, he writes shaders for fun and is quietly working toward becoming a gaming cyborg. He gravitates toward small teams, sharp problems, and systems that need to actually scale.
