Training AI to reason about the world
We build controllable game-like environments where models learn to track state over time, reason under occlusion, and predict consequences.
We build controllable game-like environments where models learn to track state over time, reason under occlusion, and predict consequences.
Most data teaches models what to say or what to do,
not how the world evolves.
Most data teaches models what to say or what to do, not how the world evolves.
We build small, fully specified
environments designed to
surface world dynamics.
We build small, fully specified environments designed to surface world dynamics.
Each environment exposes ground-truth state, transitions, and events over time — including information the model cannot directly observe. This lets researchers train and evaluate models on memory, causality, and prediction, not just outcomes.
Each environment exposes ground-truth state, transitions, and events over time — including information the model cannot directly observe. This lets researchers train and evaluate models on memory, causality, and prediction, not just outcomes.
Each environment exposes ground-truth state, transitions, and events over time — including information the model cannot directly observe. This lets researchers train and evaluate models on memory, causality, and prediction, not just outcomes.
Working on spatial or temporal reasoning?
Most data teaches models what to say or what to do, not how the world evolves.
We work with research teams building models that must understand dynamics, memory, and hidden state. Tell us what you’re training — we’ll show you what our environments enable.
Most data teaches models what to say or what to do, not how the world evolves.
FAQ
Learn more about what Nitrode has to offer or feel free to contact us at support@nitrode.com
What kind of data does Nitrode provide?
What kind of data does Nitrode provide?
What kind of data does Nitrode provide?
Is this data for agents or RLHF?
Is this data for agents or RLHF?
Is this data for agents or RLHF?
Why use game-like or synthetic environments?
Why use game-like or synthetic environments?
Why use game-like or synthetic environments?
Do you offer custom environments or datasets?
Do you offer custom environments or datasets?
Do you offer custom environments or datasets?