Pebblous Investment Proposal

Data Greenhouse Strategy
for the Physical AI Era
Pebblous Inc. - CONFIDENTIAL

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CONFIDENTIAL

Data Greenhouse Strategy
for the Physical AI Era

"Capturing the Physical AI Market with Green Data"

Oct 2025 · Pebblous Inc.

CONFIDENTIAL · 한국어

Executive Summary

As AI technology becomes commoditized, the market is clearly bifurcating. The 'general-purpose AI' market where IT companies self-develop has become a red ocean, while a massive blue ocean is opening in 'non-IT core industries' that lack AI development capabilities but have explosive adoption demand.

The success of Physical AI (robotics, autonomous driving, smart factories, shipbuilding, defense) depends not on models but on data. The three major data bottlenecks in Physical AI are: scarcity (data that does not exist in reality), security (on-premise environments required), and complexity (real-time processing of multimodal data).

Pebblous presents the 'Data Greenhouse' vision to capture this specialized market. Our GTM strategy is Mobility-First: from Hyundai Motor Group reference to global mobility expansion to shipbuilding/defense market capture, growing as the green data provider for the Physical AI era.

1. The Physical AI Paradigm: Why Now?

Market Bifurcation of AI

The advancement of GenAI and LLMs has brought an 'upward leveling' of AI technology. Companies with high IT capabilities develop AI solutions in-house rather than purchasing them. This marks the end of the 'general-purpose AI solution' market and the beginning of a massive blue ocean in 'non-IT' core industries (manufacturing, shipbuilding, defense) that cannot build AI independently but urgently need it.

Physical AI Market Opportunities

  • Robotics: The ChatGPT moment for general-purpose robotics is approaching
  • Autonomous Driving: AI systems that understand the physical world
  • Manufacturing/Shipbuilding/Defense: Core industries unable to build AI independently

"Physical AI can perceive, reason, plan, and act. The ChatGPT moment for general-purpose robotics is coming soon."
-- Jensen Huang, NVIDIA CEO, CES 2025 [6]

At the Heart of National Strategy

The Korean government has designated 'Becoming the #1 Physical AI Nation' as a core objective in its 15 flagship projects for national AI transformation.[1]

Key Government Targets (15 Flagship Projects)

  • Humanoid Industry: Top 3 globally by 2030
  • Autonomous Vehicles: Commercialization by 2027
  • Data Market: KRW 50 trillion scale by 2030

Robotics, autonomous driving, and smart factories are directly tied to national manufacturing competitiveness, with concentrated budgets and policy support driving explosive market growth.

From Model-Centric to Data-Centric

The universalization of foundation models is transforming the AI development paradigm. 'Data quality and acquisition' has become the core competitive advantage.[2]

Key Elements of Data-Centric AI

  • Data Quality: Ensuring accuracy, consistency, and completeness
  • Data Labeling: High-quality annotations and metadata
  • Data Diversity: Covering edge cases and long-tail scenarios

Physical AI especially relies on data from the physical world, which cannot be obtained through simple web scraping. This is the domain where data value is maximized.

2. Core Problems: Physical AI Data Bottleneck

Problem #1

Data Scarcity

For Physical AI to operate in real environments, long-tail data is essential. However, rare defect cases, robotic exceptions, and autonomous driving hazards are nearly impossible to obtain in reality.

Real-World Data Scarcity Examples

  • Smart Factory: Rare defect patterns with annual occurrence below 0.001%
  • Autonomous Driving: Impossible to reproduce hazardous situations on real roads
  • Robotics: System halts during exceptions, preventing data collection

As a result, AI models lack resilience against edge cases and exceptions, making real-world deployment difficult.

Problem #2

Security & Governance Constraints

Data from manufacturing, shipbuilding, and defense industries represents core technical assets directly linked to national security. Such data absolutely cannot leave on-premise environments.

Industries Requiring On-Premise

  • Hyundai Motor: Vehicle design/manufacturing data export prohibited
  • Shipbuilding: Classified ship blueprints and construction processes
  • Defense: Highest security classification for weapons systems data

Furthermore, internal data governance policies themselves require security. Data collection, processing, and utilization policies are core know-how directly tied to AI competitiveness.

This limits the use of cloud-based AI tools, significantly reducing data processing and AI development efficiency.

Problem #3

Multimodal Data Complexity

Physical AI must integrate heterogeneous multimodal data in real time to understand the physical world. Synchronizing and processing data from sensors, vision cameras, time-series logs, and 3D spatial data with different formats and frequencies is extremely complex.

Multimodal Data Integration Challenges

  • Sensor Fusion: Synchronizing LiDAR, radar, IMU, GPS and more
  • Time-Series Alignment: Aligning data with different sampling rates
  • Real-Time Processing: Handling high-volume data streams at millisecond latency

Most companies fail at building data pipelines, causing AI projects to stall entirely.

Key Insight

The true moat of the Physical AI era is not the model. The 'data pipeline' that collects, refines, and generates scarce, sensitive, and complex data is the greatest competitive advantage.

Only companies that can build and operate this pipeline will survive in the Physical AI market.

3. Pebblous Solution: Data Greenhouse

Pebblous's 'Data Greenhouse' is an end-to-end platform that resolves the three core Physical AI data problems on a 1:1 basis.

It represents the evolution of the 'Data Clinic' vision combined with GenAI, Agentic AI, and regulatory compliance technologies.

3.1. Solution Components

Solution #1

Hyper-Synthetic Data

Solves: Problem #1 Data Scarcity

By combining GenAI and CG technologies, we generate data that does not exist or is extremely rare in reality as ultra-high-quality 'Hyper-Synthetic Data (Green Data)'.

Hyper-Synthetic Data Use Cases

  • Humanoid: Diverse motion patterns and exception scenario data
  • Smart Factory: Rare defect cases and robotic welding simulation
  • Defense/Shipbuilding: Enemy objects in fog, hazardous scenarios

This is not simple data augmentation but the 'creation' of data that does not exist.

Solution #2

Agentic Data Clinic (On-Premise)

Solves: Problem #2 Security & Governance

AI agents powered by 'Agentic AI' technology autonomously diagnose, refine, and improve secure data within the client's on-premise environment.

Agentic Clinic Autonomous Tasks

  • Auto Diagnosis: Automatic data quality issue detection and analysis
  • Autonomous Refinement: Noise removal, outlier handling, label correction
  • AI-Ready Conversion: Automatic format optimization for model training

Fully meets on-premise requirements while also addressing data quality issues.

Solution #3

PebbloScope

Solves: Problem #3 Multimodal Complexity

3D visualization of complex multimodal data, enabling non-IT field operators to intuitively understand and communicate about data.

PebbloScope Key Features

  • 3D Visualization: Sensor, vision, and log data integrated in 3D space
  • Intuitive Interface: Usable by factory managers and shipyard directors
  • Communication Tool: Accurately reflecting field needs in AI development

This is the key communication tool that breaks down "the wall of communication and interpretation" between the field and AI teams.

3.2. Core Architecture: Edge-to-Core

The 'Data Greenhouse' operates on an 'Edge-to-Core' architecture optimized for Physical AI environments. Data pipelines for Physical AI face the unique challenge of requiring both real-time response at the edge and large-scale training at the core.[8]

At the Edge (Field)

'DataLens' optimized for edge computing collects massive real-time multimodal data from factories, robots, and vehicles, then lightens and transmits to Core.

  • Immediate data quality verification at the field
  • Real-time data compression and preprocessing
  • Network bandwidth optimization

At the Core (On-Premise)

'Data Clinic' within the client's internal network performs complex, heavy computation on data received from Edge.

  • Agentic AI Diagnosis: Autonomous data quality analysis and improvement
  • Hyper-Synthetic Generation: Mass generation of high-quality synthetic data
  • PebbloScope Visualization: Integrated 3D multimodal data display
  • Large-scale model training and analysis

3.3. Integrated AI Governance

The 'Data Greenhouse' goes beyond data processing to integrate AI regulation and governance management.

Global AI Regulations & Standards Compliance

  • EU AI Act: European AI regulation compliance
  • ISO/IEC 42001: AI Management System Standard[4]
  • ISO/IEC 5259: Data Quality Standard
  • ISO/IEC 42119: AI Testing & Evaluation Standard

In response to strengthening global regulations, it tracks data lifecycle, quality, and bias and automatically generates audit reports.

Solution Summary

Pebblous's 'Data Greenhouse' is a complete platform solving all three Physical AI data bottlenecks:

  • Hyper-Synthetic resolves data scarcity
  • Agentic Clinic addresses security & governance
  • PebbloScope solves complexity
  • Edge-to-Core architecture ensures real-time capability and scalability
  • Integrated Governance for global regulatory compliance

4. Proving Customer Value: Industry ROI Scenarios

Three scenarios demonstrate the concrete impact and ROI when Pebblous's 'Data Greenhouse' is deployed in real industrial settings.

Scenario #1

Mobility - Hyundai Smart Factory Welding Defect Detection

Key Info

  • Industry: Mobility (Automotive)
  • Client: Hyundai Motor Gwangmyeong Plant
  • Problem: Data Scarcity

Expected ROI

Investment: $1M

ROI: 8,150%+

Payback: 1.8 months

Scenario #2

Shipbuilding - Korean Shipyard Construction Schedule Optimization

Key Info

  • Industry: Shipbuilding
  • Client: Major Korean Shipyard
  • Problem: Multimodal Complexity

Expected ROI

Investment: $2M

ROI: 1,725%+

Payback: 8 months

Scenario #3

Defense - Marine Corps Tactical Training Synthetic Data

Key Info

  • Industry: Defense & Military
  • Client: ROK Marine Corps
  • Problem: Data Scarcity + Security

Expected ROI

Investment: $2M

ROI: 1,650%+

Payback: 8.3 months

Summary

Scenario Domain Investment ROI Payback
#1 Mobility $1M 8,150%+ 1.8 mo
#2 Shipbuilding $2M 1,725%+ 8 mo
#3 Defense $2M 1,650%+ 8.3 mo

Key Message

Pebblous's 'Data Greenhouse' is not just an AI tool but essential infrastructure for the Physical AI era.

  • Investment payback within 1 year across all scenarios
  • Annual ROI of 1,650% ~ 8,150%
  • Data generation period reduced from years to 2 weeks
  • Full on-premise security compliance

5. GTM Strategy: Mobility-First

A phased market entry strategy to capture the global mobility market based on Hyundai's flagship reference, then expand to shipbuilding and defense.

Phase 1

Hyundai Flagship (2026)

Establish collaboration with Hyundai Motor Group as the 'On-Prem Data Greenhouse' flagship reference

Phase 2

Global Mobility (2027)

Reference-based global mobility market expansion (BMW, Toyota, etc.)

Phase 3

Shipbuilding/Defense Expansion (2027-2028)

Capture shipbuilding and defense markets where data scarcity and AI development demand are highest

6. Investment Plan

Investment Scale

Fundraising Amount KRW 2B (~$1.5M)
Post-money Valuation KRW 20B (~$15M)

Fund Allocation

Use of Funds

On-Prem Productization 40% (KRW 800M)

Data Clinic On-Prem version development and optimization

GTM Execution 40% (KRW 800M)

Hyundai reference acquisition and global sales organization

Operations Stabilization 20% (KRW 400M)

Organizational operations and technical infrastructure maintenance

References

[1] Ministry of Science and ICT, Korea (2025). "AI/Digital Innovation Growth Strategy."

[2] Andrew Ng (2022). "Andrew Ng: Unbiggen AI." IEEE Spectrum.

[3] McKinsey & Company (2024). "The State of AI in 2024."

[4] ISO/IEC 42001:2023. "AI Management System."

[5] Stanford HAI (2024). "AI Index Report 2024."

[6] NVIDIA (2025). "CES 2025: Jensen Huang on Physical AI and Robotics."

[7] Pebblous Blog (2025). "How Data Startups Ride the Physical AI Wave."

[8] Pebblous Blog (2025). "Data Pipeline for Physical AI: Edge-to-Core Architecture."

[9] Pebblous (2025). "Pebblous Investment Proposal (v22.0)." (Appendix)

[10] S&P Global (2025). "AI in the Automotive Industry."

[11] UnitX Labs (2025). "ROI of Automated Visual Inspection Systems."

[12] Beamo.ai (2024). "Digital Twin Solutions for Shipbuilding."

[13] Military Embedded Systems (2024). "Challenges of Military Training Simulation."

[14] SKY ENGINE AI (2024). "Security and Defence Use Cases."

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Physical AI Data Greenhouse

Data Greenhouse strategy for the Physical AI era (PDF companion to this page)

Pebblous Investment Proposal (Legacy)

Data Clinic SaaS and Synthetic Data focused proposal (v21.4)

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