We are transforming
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The world runs on data, and so do companies. Yet data is the part the industry has left behind. The result is personal AI assistants, general foundational models, and pilots that never reach production.

Adopting AI across an organization, beyond buying a tool or making individuals more productive, spans infrastructure, applications, security, competence and culture. It has many failure modes: fragmented data, outdated operating models, strict regulatory requirements, and legacy systems that were never built for agents.

Underneath, the challenges with data are the same: silos, security, governance, and literacy. Still, what's available is either surface-level tools or bigger language models. And as the expectations on AI shift from promise to outcomes, the foundational work becomes harder to ignore.

AI makes the need visible and urgent, but it did not create it. It has been there all along. Data platforms, conceptual data models, integration projects and master data programs have strained the same boundaries around data for decades.

We believe the missing layer is a harness around a company's data and the agents that work on it: one that can build projects and flows on request, run long-running research tasks unattended, and search and reason over large amounts of data, while remaining observable, secure and controllable by people. We call it Data Intelligence.