Suyash Raizada

Explore NVIDIA Cosmos 3, an open foundation model for Physical AI that combines vision reasoning, world generation, multimodal understanding, and action prediction for robotics and autonomous systems.

Explore NVIDIA Nemotron 3.5 Lightning, a reasoning-capable 30B MoE model designed for efficient agentic AI, coding, tool use, long-context tasks, and low-latency inference.

Explore how Jev supports software automation with fast, typed, probabilistic decisions for routing, scoring, classification, verification, branching, and workflow control.

Explore how Jev functions as an intelligence primitive for software, providing fast, typed, probabilistic decisions that developers can combine into larger automated workflows.

Explore how Jev can function as a decision layer inside software, turning structured state and questions into typed, probabilistic outputs for automation and workflow control.

Learn how Jev’s decision-making pipeline transforms program state and structured questions into typed, probabilistic decisions that software can use for automation.

Learn how Jev achieves low-latency AI decisions through parallel sampling, structured outputs, calibrated probabilities, and System One architecture designed for real-time software automation.

Explore Jev’s inference architecture, including parallel sampling, structured outputs, calibrated probabilities, and how TypeSafe AI enables fast machine-native decision-making.

Learn how the Jev decision pipeline works, from program state and structured questions to parallel processing, typed outputs, calibrated probabilities, and automated software actions.

Learn how Jev processes multiple structured questions in parallel, enabling faster probabilistic decisions across software workflows, automation tasks, and real-time applications.

Learn how Jev generates probabilities using parallel sampling, calibrated decision training, and structured outputs designed to express uncertainty in software automation.

Learn how Jev calibrated decisions work, including typed outputs, probability estimates, confidence scores, and how software can use uncertainty for more reliable automation.