Paradome

Rapid AI development & deployment for government

Realistic RL environments for open-horizon tasks

Paradome delivers rapid prototyping, deployment, and optimization of AI automations for government agencies. Government AI procurement mirrors outdated IT practices—pay millions, wait years. The high cost and long timelines stem from (1) building data integrations, labeling operations, and compliance mechanisms from scratch, (2) lack of AI expertise among traditional contractors, (3) minimal input from end-users during development, and (4) the ongoing expense of monitoring, maintaining, and updating bespoke systems. Paradome brings the time and cost of deploying compliant AI to near-zero, enabling agencies to spend time and money on iteration rather than development. Agencies need a single, versatile environment that combines agile low-code tools with full-code extensibility. Paradome’s NOVA modularizes data integration, pipeline optimization, accuracy testing, federal compliance, and real-time monitoring, eliminating bottlenecks and accelerating AI deployment. Sam's ex-girlfriend introduced him to Greg back at Carnegie Mellon in 2017, and while that relationship didn't last, their friendship has. After college, Greg went to Harvard Law School, while Sam worked for three years at Jane Street, building & leading a satellite dev team. Agencies provide public services and enforce the law. With AI, their work can be consistent, impartial, and efficient. Making government work is not a political choice—it’s a civic duty.

We are entering what Rich Sutton terms the “Era of Experience”, where agents learn from continuous feedback in a realistic environment. In our view, the key bottleneck to entering this era is high-feedback realistic environments which agents can inhabit. So far, coding and math have proven to be rich task-horizon environments, but we believe that open-horizon environments are the critical next step — environments with a continuous, open goal, much like the world we live in. At Paradome, we're focusing on one of the highest-feedback open environments in the world: trading. We believe trading is one of the best domains to train models to develop research taste and intuition based on real-world continuous feedback. We're building the infrastructure to make this happen. Sam's ex-girlfriend introduced him to Greg back at Carnegie Mellon in 2017, and while that relationship didn't last, their friendship has. After college, Greg went to Harvard Law School, while Sam worked for three years at Jane Street on their Options desk, building & leading a satellite dev team.