Headquarters
South Korea
Product direction, governance, and customer delivery.
About Origindata Lab
We build datasets from
real-world urban complexity.
High-entropy streets where structure must be discovered, not assumed.
We capture complex urban environments as they are.
Noise, ambiguity, and human behavior are not filtered out — they are the data.
See real-world failure scenarios before committing to a full dataset.
No payment. No commitment. Reply within 24 hours.
Built as an early-stage data pipeline — focused on speed, flexibility,
and real-world complexity.
Early Access Program
Work with us as an early evaluation partner
We’re currently onboarding a limited group of early evaluation partners.
Each partner’s use case directly influences what we collect next — including location, traffic scenarios, and edge cases.
This is not a passive dataset purchase. It’s a chance to shape a real-world collection pipeline around your model needs.
Internal Validation Snapshot
We are building internal benchmarking workflows to compare baseline open datasets against dense urban footage collected through our own field pipeline.
- Dense intersections, mixed traffic, occlusion-heavy scenes
- Focus on pedestrians, motorbikes, and small-object detection
- Goal: improved recall in cluttered environments
Team Structure
Our team operates across field capture, QC, and structured dataset delivery.
- CEO — data strategy and partner collaboration
- Data Engineer — ingestion & QC pipeline
- Field Ops — distributed capture teams
- QC Specialist — segment validation
- BD — evaluation partner onboarding
Instead of hiding our early stage, we make our process visible — so partners can evaluate how we collect, structure, and adapt datasets in real time.
How We Operate on the Ground
Origindata Lab combines operational coordination with field-based data collection. Our workflow connects planning, quality control, and live urban capture so that raw footage is collected with structure, consistency, and real-world relevance from the beginning.
The team combines hands-on field operations with technical understanding of dataset structure, quality control, and delivery requirements for AI training and evaluation workflows.
Our data is collected through a distributed field network across multiple cities in South and Southeast Asia, using a proprietary capture workflow and centralized quality control pipeline operated from our data operations center.
We are currently working with early evaluation partners, where each use case directly informs our next collection and delivery cycle.
Our team combines field operations, data structuring, and quality validation workflows tailored for AI training environments.
A fully controlled, end-to-end data pipeline — from real-world capture to structured delivery.
Our capture app systematically collects video and rich metadata directly from real-world environments.
From field capture to structured dataset delivery, all steps are controlled within our system.
Centralized infrastructure ensures scalable processing, reliability, and secure data handling.
Rich metadata is refined through automated pipelines into precise, production-ready datasets.
How We Collect Data at Scale
Our data is not collected from a single location. It is continuously captured across multiple high-entropy urban environments through a distributed field network.
All captured data is centrally aggregated and processed through our Singapore-based infrastructure, ensuring consistency, scalability, and production-ready quality.
Field operators capture real-world urban environments across multiple cities simultaneously.
All collected data is securely aggregated into our Singapore-based infrastructure for processing.
Training, Standards, and Field Readiness
Reliable collection depends on repeatable standards. Our workshops align teams around capture discipline, equipment handling, field procedures, and quality expectations so that data collection remains usable at scale across different cities.
Where we operate
Built in Korea • Collected across Asia • Delivered through cloud-first infrastructure in Singapore.
Data Collection
Asia
High-entropy city environments across South & Southeast Asia.
Infrastructure
Singapore
Cloud-first storage and processing for fast, reliable delivery.
Who This Data Is Built For
This data is designed for teams building, testing, and validating systems that must perform reliably in complex, high-entropy urban environments.
Autonomous Driving & ADAS
Teams working on perception, prediction, and planning systems that must operate in dense, mixed-traffic, and behavior-driven environments.
Robotics & Embodied AI
Robotics and embodied AI teams requiring real-world interaction data, occlusion handling, and human-centric motion understanding.
Applied Research & Evaluation
Applied research groups and ML teams conducting robustness testing, failure analysis, and real-world model validation.
Trust is designed, not claimed
Consent, provenance, and usage boundaries belong in the pipeline — not in a PDF after the fact. We build with clear sourcing logic, permissions, and intended-use boundaries from the start.
As policies evolve, the structure remains traceable, auditable, and easier for customers to evaluate with confidence.
Current Coverage Snapshot
A high-level view of our current operational dataset coverage. Detailed scenario breakdowns, validation samples, and release-specific materials are shared on request.
Recorded Volume
1,800+ hours of continuous urban footage
Captured through continuous daily collection operations and organized into structured monthly snapshot releases.
• Continuous daily data collection
• Structured monthly snapshot releases
• Continuous source footage preserved for segment extraction
Scenario Coverage
25+ tagged high-entropy scenario categories
Coverage is designed around complex real-world urban failure cases that are difficult to reproduce in simulation-only pipelines.
• Non-lane-based traffic environments
• Dense pedestrian and mixed-actor interactions
• Motorbike-heavy urban traffic flows
• Occlusion-rich and interaction-dense scenes
Geographic Coverage
Operational coverage across South & Southeast Asia
Active collection infrastructure is already operating across key cities, with expansion capacity extending into Africa and Central Asia.
• Operational coverage across key cities in South & Southeast Asia
• Active collection infrastructure extending into Africa and Central Asia
• Regional expansion based on partner demand and project scope
Need a real-world dataset partner for complex urban environments?
We can share current coverage, data structure, and sample materials based on your evaluation needs.
See how your model performs in real-world failure scenarios before scaling.
No payment. No commitment. Reply within 24 hours.