Executive Summary
The era of Physical AI -- autonomous vehicles, humanoid robots, and smart factories -- has arrived. Yet the data most critical to AI safety, such as vehicle-pedestrian collisions, robotic malfunctions, and disaster scenarios, is virtually impossible to collect in the real world. To solve this 'Data Famine' problem, Pebblous proposes a new paradigm: instead of 'hunting' for data, we 'cultivate' it.
Pebblous's Data Greenhouse intentionally generates the data needed within digital twin-based virtual environments that faithfully replicate real-world physics. Its core execution engine, PebbloSim, combines physics simulation with generative AI through a 'Neuro-Symbolic' approach, producing high-quality synthetic data free from Physical Hallucination.
The Data Greenhouse operates as an autonomous system of Observe-Prescribe-Act-Prove, recording the entire process as 'Operational Evidence' -- from ISO 5259-based data quality diagnosis to EU AI Act and ISO 42001 regulatory compliance. Pebblous is currently validating the future of data alongside leading companies in automotive, defense, shipbuilding, and robotics.
1. No Seed, No Harvest
Autonomous vehicles, humanoid robots, smart factories. The era of Physical AI has arrived. But behind the grand vision lies a critical bottleneck.
The paradox: the most important data simply cannot be collected.
The moment an autonomous vehicle collides with a pedestrian, a robotic arm malfunctions, or a disaster strikes -- this 'Edge Case' data is essential for teaching AI to be safe. But we cannot deliberately cause accidents in the real world.
We call this 'Data Famine'. The hunter-gatherer approach of searching for data in the wild can never solve this famine.
2. Cultivating Data in a Greenhouse
A farmer can harvest tomatoes even in winter because of the greenhouse. The greenhouse does not defy nature -- it optimizes growth in a controlled environment.
Pebblous's Data Greenhouse applies this philosophy to AI data. Within virtual environments (Digital Twins) that perfectly replicate real-world physics, we intentionally create the scenarios we need. A digital twin is not a simple 3D model -- it is a physics engine that simulates gravity, friction, collision, and inertia identically to reality.
3. Data Without Physical Hallucination
Standard generative AI produces plausible-looking images but knows nothing about physics. Cars floating in mid-air, inconsistent shadows -- such 'Physical Hallucination' is a lethal poison for Physical AI.
Pebblous's execution engine, PebbloSim, takes a 'Neuro-Symbolic' approach.
| Category | Standard Generative AI | PebbloSim |
|---|---|---|
| Core Principle | Pixel probability prediction (drawing pictures) | Physics simulation + rendering (building structures) |
| Controllability | "Rainy road accident" (vague text) | Friction coefficient 0.3, collision angle 45 degrees (precise parameters) |
| Output | Images containing hallucinations | Explainable, physically accurate data |
In simple terms, we first build the skeleton (physics simulation) and then overlay the skin (generative AI). Because the skeleton is accurate, the data is physically sound, and when AI fails, the cause can be explained through engineering principles.
4. Autonomous Agent-Managed Data
The Data Greenhouse is not merely a factory. It is an autonomous operating system (OS) that manages the health of your data.
In particular, the Observe stage applies ISO/IEC 5259 (Data Quality for AI) standards, measuring accuracy, completeness, consistency, and timeliness against international benchmarks and quantitatively identifying gaps.
When regulations like the EU AI Act or ISO 42001 ask "Is your AI safe?", the Data Greenhouse answers:
"We diagnosed critical data gaps using ISO 5259 standards, reinforced them with physically verified data, and here is the 'Operational Evidence' documenting the entire process."
5. Partnership Proposal
Success in the Physical AI market depends not on models, but on who holds the most valuable data.
Pebblous is currently validating the future of data alongside leading companies in the automotive, defense, shipbuilding, and robotics sectors.
In particular, through the 'AI Global Big Tech Development Program' led by the Ministry of Science and ICT of Korea, the AADS (Agentic AI Data Scientist) Phase 2 project targets Physical AI data as its core focus. Through multimodal synthetic data generation technology and manufacturing-specialized Sovereign VLM (Vision-Language Model) development, we aim to elevate Korea's AI competitiveness as a manufacturing powerhouse.
We welcome inquiries from major manufacturing companies that need high-quality synthetic data with guaranteed physical consistency, and from partners who want to co-build a reliable AI data infrastructure.
We don't collect data. We cultivate it.
A Data Operating System for Physical AI