We believe that software systems should be able to exchange data reliably, even under the most complex circumstances. We work tirelessly to bring this reality to organizations around the world.
Conexus provides solutions that enable data architects and IT leaders to overcome intractable challenges related to large scale data integration. For organizations trying to reconcile many diverse data structures, Conexus enables reliable and scalable data model integration that results in IT semantic interoperability. Our technology solutions perfectly compliment domain-driven architecture and a data mesh enterprise strategy.
Exploiting breakthroughs in Category Algebra, Conexus technology approaches data integration differently. The technology enables an incremental domain-driven approach to data interoperability where the optimal universal data model is computed, not designed. The result is data integration pathways that are exponentially cheaper to construct, better at protecting data integrity, and more adaptable to change.
Founded by Eric Daimler, David Spivak, and Ryan Wisnesky, our team has a rich history in data science and Categorical Mathematics. We bring a high attention to detail and mathematical rigor to the work we do, so that you can trust the conclusions drawn from your data. We take pride in building solutions that help our clients achieve new outcomes and make better use of their data at the largest scales.