Infinite worlds.
Infinite intelligence.

Infinite worlds.
Infinite intelligence.

Infinite worlds.
Infinite intelligence.

Backed By

We train foundation models and build infrastructure for world generation, task authorship, and model evaluation, enabling high-fidelity environments that transfer human expertise into model capability.

We train foundation models and build infrastructure for world generation, task authorship, and model evaluation, enabling high-fidelity environments that transfer human expertise into model capability.

Jigsaw is an applied research lab building infrastructure to automate the creation of simulations for frontier labs and enterprises.

Jigsaw is an applied research lab building infrastructure to automate the creation of simulations for frontier labs and enterprises.

Jigsaw is an applied research lab building infrastructure to automate the creation of simulations for frontier labs and enterprises.

our products

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The Platform for Scalable RL Environments

Domain experts contribute directly to model intelligence, no engineers in the loop. Specialized RL environments tailored to your enterprise.

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Darwin

The Platform for Scalable RL Environments

Domain experts contribute directly to model intelligence, no engineers in the loop. Specialized RL environments tailored to your enterprise.

Darwin

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The Platform for Scalable RL Environments

Domain experts contribute directly to model intelligence, no engineers in the loop. Specialized RL environments tailored to your enterprise.

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Darwin

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The Foundation Model for World Generation

Generate high-fidelity environments from internal workflows and domain knowledge. Realistic worlds, tools, and interfaces for training and evaluation.

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Pascal

The Foundation Model for World Generation

Generate high-fidelity environments from internal workflows and domain knowledge. Realistic worlds, tools, and interfaces for training and evaluation.

Pascal

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The Foundation Model for World Generation

Generate high-fidelity environments from internal workflows and domain knowledge. Realistic worlds, tools, and interfaces for training and evaluation.

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Pascal

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Data trained the last generation. Environments will train the next.

By late 2026, every serious AI company will need continuous RL training on domain-specific workflows. Enterprises, neo-labs, and frontier model teams will all fine-tune on internal processes. The bottleneck is environments.

By late 2026, every serious AI company will need continuous RL training on domain-specific workflows. Enterprises, neo-labs, and frontier model teams will all fine-tune on internal processes. The bottleneck is environments.

We own that bottleneck. Our simulated worlds let teams evaluate agents on real business processes before deploying them to production.

We own that bottleneck. Our simulated worlds let teams evaluate agents on real business processes before deploying them to production.

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Data trained the last generation. Environments will train the next.

By late 2026, every serious AI company will need continuous RL training on domain-specific workflows. Enterprises, neo-labs, and frontier model teams will all fine-tune on internal processes. The bottleneck is environments.

We own that bottleneck. Our simulated worlds let teams evaluate agents on real business processes before deploying them to production.

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Scaling worlds to

unlock real-world intelligence.

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© 2026 jigsaw

company

Careers

Research

The final primitive for superintelligence is learning through simulated environments. That is how models became strong in coding and math. The next frontier is the rest of human labor and interests.

Image

Scaling worlds to

unlock real-world intelligence.

Image

© 2026 jigsaw

company

Careers

Research

The final primitive for superintelligence is learning through simulated environments. That is how models became strong in coding and math. The next frontier is the rest of human labor and interests.

Image

Scaling worlds to

unlock real-world intelligence.

Image

© 2026 jigsaw

company

Careers

Research

The final primitive for superintelligence is learning through simulated environments. That is how models became strong in coding and math. The next frontier is the rest of human labor and interests.