Founding Thesis and Team

As data becomes central to operating companies, data platforms have become swamps of spaghetti code and datasets. Lacking reusable and composable blocks that represent business semantics, data teams are perpetually constrained writing and rewriting logic for various use cases.

This not only drains the productivity of the data team, business teams end up waiting in long request lines and flying blind. Most surprisingly, as orgs are pushing the usage of data, "BI" tools are stuck generating isolated metric calculations in reports - the complex connectivity across the organization is completely ignored, and the high value work of planning metrics, root-causing drivers, running experiments, measuring outcomes are all happening off-system in manual workflows, if at all.

Every technological revolution goes through standardization and automation - and on the right abstractions, we believe the modern data clouds can become engines that drive business performance. These abstractions involve entities, relationships, metrics, attributes, segments, experiments etc. And, automating common and complex metric transformations using these business semantics unlocks productivity for the data team, provides the right interface for the business teams, and directly supports high value workflows.

Our founding team has deep experience in building data-centric orgs at Uber, Rent the Runway, Google, and Bitly. We are backed by Amplify and FirstMark - top tier investors in the data/dev space - to drive the next wave of the data revolution.