WORLD DATA INFRASTRUCTURE

Training data for models that understand the world.

We transform simulated and real-world environments into structured data engines that power world models, robotics, and physical AI.

From licensed game worlds and virtual assets to synchronized video, actions, state, and executable evaluation environments.

Start a Targeted Dataset Explore Data Engines →

Operating across Singapore and San Francisco, with engineers from NVIDIA, Qualcomm, miHoYo, and world-class research institutions. Supporting confidential programs for leading world-model, game, embodied AI, and robotics teams.

Two worlds. One data layer.

We transform virtual and physical systems into production-ready training data — from targeted datasets to customer-specific data engines.

Virtual World Data

Structured training and evaluation data from customer-authorized game environments, licensed 3D worlds, virtual assets, simulations, and controllable interactive environments.

For teams that need spatially structured worlds, gameplay trajectories, state access, controllable scenarios, and repeatable evaluation data.

Engine-Native World Model Data

Synchronized video, actions, camera, telemetry, events, and world state captured directly from customer-authorized game and simulation environments.

Delivered as:

frame-aligned records · action-state trajectories · camera paths · event logs · replayable episodes

For: video world models · action-conditioned generation · spatial intelligence · post-training and evaluation

Executable 3D Environments for AI Agents

Playable browser-native worlds with source code, licensed virtual assets, runtime-state interfaces, behavioral contracts, and repeatable evaluation.

Delivered as:

Three.js environments · GLB assets · state schemas · benchmark tasks · interaction traces

For: coding agents · game-generation models · multimodal agents · executable world evaluation

View a Sample Record →

Embodied AI Data

Action-state data for robots, agents, and physical AI systems.

For teams that need grounded actions, recovery behavior, and evaluation loops.

Delivered as:

demonstrations · action-conditioned video · manipulation traces · task states · failure recovery · evaluation sets

For: robotics, VLA, autonomous systems, and real-world deployment teams

From targeted datasets to customer-specific data engines.

How we engage.

Scoped delivery for teams that need targeted datasets, not slide decks.

Targeted Datasets

Scope, collect, annotate, clean, and validate the dataset a model needs — then deliver it as a versioned, production-ready record.

Scalable Data Engines

Build and operate customer-specific pipelines for collection, curation, annotation, validation, versioning, and continuous multimodal production.

Confidential Frontier Work

Delivery under strict confidentiality for proprietary worlds, robot behaviors, model goals, unreleased systems, and internal datasets.

One environment. Every signal your model needs.

From a virtual world, simulation run, or robot task, we produce synchronized signals as one coherent training record.

Geometry

Calibrated views, surfaces, and spatial structure.

Latest Insights

Research notes and field perspectives on game data, virtual worlds, spatial intelligence, and AI training infrastructure.

3D Assets

What Makes a 3D Asset Dataset Ready for AI Training?

Geometry, topology, materials, scale, orientation, captions, rendering views, validation, and provenance — the fields that turn a mesh dump into 3D training data.

August 28, 2026 · 4 min read

Research Notes

WorldCraft: From Camera Navigation to Object Trajectories — Data Requirements for Manipulable Worlds

Independent notes on WorldCraft’s world-space object trajectories, camera-object composability, and the annotation fields production datasets need for off-camera state.

August 27, 2026 · 6 min read

Research Notes

AlayaWorld and the Data Behind Playable Video World Models

An independent analysis of AlayaWorld and the gameplay video, actions, state, and temporal data behind long-horizon playable world models.

August 27, 2026 · 7 min read

View all insights Explore Virtual Assets for AI →

Pauca sed matura.

Few, but mature.

— Carl Friedrich Gauss

We do not optimize for generic volume. We design data around the world state a model must infer, the dynamics it must predict, and the actions it must ground — then deliver scalable, versioned data engines for post-training, evaluation, and closed-loop improvement.

World-state first

We start from the state, dynamics, and interaction structure a model needs to learn.

Engine-ready

We deliver versioned, reusable data for post-training, evaluation, and continuous improvement — not one-off exports.

Confidential by default

Customer environments, model goals, and technical details remain protected throughout delivery.

What does your model need to learn?

Bring us the environment, behavior, failure mode, or data engine you need to build. We’ll scope a program around it.

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Making a dot in the universe.

madiu@gaussmathematics.ai