With 87% of models failing to reach production, financial institutions are forced to make decisions using stale signals. Domino bridges this last-mile gap, instantly turning validated models into secure applications.
Every model that clears governance review and goes undeployed represents uncaptured value. Application development is the mechanism by which model investment creates business impact.
Applications hosted on Domino automatically inherit the active user's permissions. This eliminates the need to hardcode database access logic and ensures the system logs exactly who authorized a transaction.
Performance degradation during high-volume market events represents a serious operational risk. Domino auto-scales compute resources horizontally within seconds to maintain decision consistency, then scales back down to control costs.
Every app version tied to the exact code and data that produced it means any regulatory question about a past decision can be reconstructed precisely, regardless of how much time has passed.
Governance controls should act as publishing gates rather than development hurdles. Quantitative analysts can iterate freely in their workspaces, while administrators enforce compliance reviews before the app goes live.
Quantitative and risk teams invest massive effort into validating complex models, yet 87% of these models never reach production. For critical functions like fraud detection, a multi-week deployment delay forces the business to make risk decisions using stale transaction data. Domino’s App Factory solves this last-mile problem by unifying model development and application hosting on a single platform.
Quantitative analysts can continue coding in their preferred IDEs with AI assistants while the platform automatically packages their work. The results are delivered as interactive, secure applications built on standard frameworks like Streamlit, Shiny, or Flask, completely eliminating manual engineering handoffs.
To satisfy strict regulatory requirements, Domino weaves auditability, reliability, accountability, and observability directly into the deployment process. In a live demonstration of an over-the-counter (OTC) trading application, Matt Bonyak showed how the platform registers every lifecycle event, requires explicit user consent to inherit data access permissions, and logs all database queries. By embedding these rigorous controls, Domino allows quantitative teams to securely scale their models from small development environments to tens of thousands of business users.
