AI-native applications combine predictive models, generative AI, and deterministic logic inside one governed workflow, replacing the API as the primary unit AI value is delivered through. This shift raises the bar on audit trail and reproducibility for regulated enterprises.
Enterprise AI teams now deliver value through fit-for-purpose applications rather than deploying models for other systems to call, changing how AI capabilities reach end users.
A single insurance claims application combines a language model reading unstructured input, a fraud model scoring risk, and deterministic routing rules, with the full pipeline observable and reproducible.
Audit trail, reproducibility, and deployment gating built into the platform let a risk management team trust and approve an application, rather than surfacing as rework at the end of development.
Domain experts increasingly build their own tools directly, without waiting on a dedicated engineering team to develop them, as coding barriers fall.
Extensions let customers build tailored capabilities inside Domino at a fraction of the cost of custom engineering, inheriting security and audit properties from the platform automatically.
Choose a platform with strong foundational capabilities, then build only what differentiates the business on top of it, since software now ships far faster than two years ago.
Nick Elprin, Domino's co-founder and CEO, opened Rev saying that enterprise AI has crossed into a new delivery model. Two forces, an expanded set of tasks software can automate and a sharp acceleration in how fast that software is built, have shifted the primary unit of AI value from models called through an API to fit-for-purpose applications used directly by business teams.
Elprin demonstrated this shift through an insurance claims processing app that combines a language model handling unstructured input, a validated fraud model scoring risk, and deterministic routing logic in one observable, reproducible pipeline.
For regulated enterprises, the harder problem is closing the gap between a working prototype and a production-ready application: reproducibility, auditability, and governance that a model risk management team can trust and defend in an examination.
Domino introduced Extensions, a capability that lets customers build tailored tools inside Domino that inherit its governance and security automatically, reframing the build-versus-buy decision around buying the right platform and building only what differentiates the business. As Nick Elprin put it, AI investment turns into AI impact only when the operational foundation is in place.
