Managed Field Collection
Project-specific environments, participants, workflows, and capture protocols organized around the buyer's target scenarios.
Custom Real-World Data Production for Physical AI and Intelligent Systems.
We design and operate managed data collection programs for robotics, computer vision, autonomous systems, and Physical AI,
from field capture to engineering-ready delivery.
- Egocentric, real-world video, sensor, and multimodal data collection
- Project-defined capture protocols, metadata, QC, traceability, and structured delivery
- Field production in South Korea and Bangladesh, from scarce workflows to scalable collection
From field capture to validation, traceability, and buyer-ready delivery.
Define the data your model is missing. We design the collection around it.
Managed Data Production
From Field Requirements to Engineering-Ready Data
We turn project-specific data requirements into managed collection programs—from capture design and field operations to validation, metadata, and structured delivery.
Multimodal Capture Pipeline
Real-world and egocentric video with project-defined sensor data, timestamps, device context, and structured capture metadata.
QC, Traceability & Delivery
Technical validation, metadata checks, source traceability, recapture workflows, and engineering-ready delivery packages.
Capture Modalities
One Model Requirement. Multiple Capture Architectures.
Origin Data Lab is not limited to a single camera, sensor, or data modality. We configure real-world collection around the task, environment, hardware, synchronization requirements, metadata schema, and model objective.
01
Proven Capture
Egocentric & Human Demonstration
First-person activity, hand-object interaction, tool use, bimanual workflows, video and IMU.
02
Project Configurable
Stereo & Multi-View
Synchronized stereo and multi-camera capture for spatial and viewpoint-aware learning.
03
Project Configurable
Wearable & Human Motion
IMU, hand, finger and body-motion sensing aligned with real-world tasks.
04
Project Configurable
RGB-D & Spatial Capture
Depth-aware capture for spatial perception, manipulation, and environment understanding.
05
Project Configurable
Robotics & Teleoperation
Robot-linked observation, state, action, trajectory and sensor data.
06
Proven Field Capability
Mobility & Computer Vision
Road, traffic, pedestrian, detection, tracking and perception data.
Existing Mobility Data Evidence
See the Data, Ground Truth, and Metadata We Actually Deliver
A production-proven mobility collection example from our Bangladesh field pipeline. This evidence demonstrates our capture, metadata, QC, traceability, and delivery workflow. Physical AI and egocentric programs are scoped separately around customer-defined tasks and sensors.
16,000+
Structured Segments
5,300+
Source Recordings
100+
Hours Collected
Collection continues through our active field pipeline and can expand according to project scope, target environments, and delivery requirements.
Industrial Field Production
Managed collection in real production environments, capturing human workflows, tool interaction, and task execution under operational conditions.
Egocentric Task Capture
First-person capture of precision human workflows, including hand-object interaction, tool use, task sequences, and operational context in real production environments.
Structured Engineering Metadata
Clip-level engineering metadata including timestamps, GPS, IMU, scene context, capture properties, object statistics, privacy processing, quality indicators, and delivery status.
timestamp
GPS
IMU
scene
device
quality
delivery
Preview fields are simplified for readability. Click the metadata card to inspect the complete website-safe JSON record. Final schemas and delivery formats are defined against each project’s technical requirements.
Collection Capabilities
Built for Real-World Physical AI Data
We build project-specific collection programs for real environments, human activity, operational workflows, and multimodal capture requirements that are difficult to reproduce through public datasets or simulation alone.
Egocentric & Human Activity
First-Person Data for Human-Centered Tasks
Head-mounted, chest-mounted, and project-defined first-person capture for household, workplace, service, manipulation, and task-oriented activities.
Industrial & Commercial
Operational Workflows in Real Environments
Collection programs for commercial kitchens, cleaning, light manufacturing, hospitality, retail, food processing, and other operational environments.
South Korea
Scarce, High-Value Regional Workflows
Access to region-specific environments and culturally distinctive workflows, including Korean food production, seafood processing, service operations, household activity, and specialized commercial tasks.
Regional Scale
Pilot Locally. Scale Through Field Operations.
Projects can begin with focused calibration and workflow validation, then expand through managed field operations in Bangladesh and other project-approved locations as production requirements grow.
Capture format, sensor requirements, participant profile, environment, workflow, metadata schema, and quality criteria are defined for each project.
Explore Our Data Capabilities: Physical AI Data Collection · Robotics Training Data · Egocentric Human Demonstration · Multimodal Sensor Data · South Korea Data Collection
What We Deliver
Real-World Data Built for Model Development
We deliver more than raw recordings. Every collection is prepared for engineering evaluation, model development, and long-term data operations.
Targeted Scenario Production
Real-world environments collected around your deployment challenges,
missing edge cases, and failure scenarios.
Multimodal Capture
Smartphone-based video capture with GPS, IMU, timestamps,
route context, device information, and recording metadata
linked to each source recording.
Structured Production Output
Quality-controlled datasets packaged with documentation,
validation, metadata, and delivery formats ready for engineering workflows.
Typical Commercial Delivery Package
dataset_package/ ├── clips_blurred/ │ └── *.mp4 │ ├── human_gt_preview/ │ └── *.jpg │ ├── metadata/ │ └── *.json │ ├── buyer_catalog.json ├── buyer_sample_index.csv ├── sample_index.csv ├── dataset_manifest.json ├── release_context.json │ ├── README.md ├── DATASET_CARD.md ├── QUALITY_ASSURANCE.md ├── PRIVACY_REPORT.md ├── RELEASE_CERTIFICATE.md ├── LICENSE.md │ └── SHA256SUMS.txt
Typical commercial deliveries include engineering documentation, structured metadata, validation records, licensing information, privacy documentation, release certificates, and integrity verification. Some annotation assets and project-specific deliverables are provided according to the agreed project scope.
Real-World Data Challenges
Capture the Conditions Models Rarely See
Real environments contain variation, ambiguity, occlusion, and human behavior that are difficult to reproduce through simulation or standardized datasets. We design collection programs around these missing conditions.
Complex Human Manipulation
Hands, tools, objects, materials, and multi-step actions create fine-grained interactions that are critical for robotics, egocentric learning, and Physical AI systems.
Operational Variability
Real people perform the same task with different motion, timing, technique, tools, workspace layouts, and environmental context. Collection can be designed to capture this natural variation.
Occlusion & Uncontrolled Environments
Partial visibility, changing lighting, clutter, moving people, complex backgrounds, and unpredictable environmental conditions expose weaknesses that clean benchmark data may not reveal.
Engineering Delivery
Structured Delivery Packages for Engineering Teams
Accepted data is organized into a traceable delivery package with media, metadata, documentation, validation records, and integrity files defined around the buyer's technical requirements.
Available
Core Data & Sensor Records
Primary data assets linked through stable clip and source identifiers for traceable engineering use.
- Video files — MP4
- Structured metadata — JSON
- Dataset indexes — CSV
- GPS and route records
- IMU sensor records
Pack or Project Dependent
Human GT & Annotation Formats
Human-verified ground truth is included only where specified. Annotation exports are defined against the project scope.
- Human-verified Ground Truth
- Bounding-box annotations
- COCO export — project-dependent
- YOLO export — project-dependent
- Custom class definitions
Commercial Delivery
Documentation & Integrity
Buyer-facing documentation and verification files support review, reproducibility, and controlled dataset handoff.
- Dataset Manifest
- Data Dictionary
- QA Report
- SHA-256 Checksums
- Commercial delivery package
Enterprise Delivery Controls
Privacy Processing
Face and license plate blurring where required.
QA & Validation
Documented quality review before release.
Source Traceability
Segments linked to source and collection context.
Licensing & Integrity
Commercial terms, manifests, and SHA-256 checksums.
Privacy scope, permitted use, annotation formats, custom schemas, delivery cadence, and licensing terms are confirmed before release.
How We Build Custom Data Programs
Every project is operated as a managed production workflow—from requirement definition and capture setup to field execution, validation, recapture, and structured delivery.
Built for AI Teams Working On
Robotics
Physical AI
Computer Vision
Autonomous Systems
Egocentric Learning
01 — Define. Target task, environment, participants, sensors, metadata, and acceptance criteria.
02 — Calibrate. Validate capture setup, instructions, and pilot recordings before production.
03 — Produce. Operate managed field collection using the approved workflow.
04 — Validate. QC submissions, track failures, and route required recaptures.
05 — Deliver. Package accepted data into buyer-defined schemas, metadata, and manifests.
Define clearly.
Capture consistently.
Validate systematically.
Deliver with traceability.
Why AI Teams Work With Us
Teams can start with a focused calibration or evaluation batch, validate the collection protocol and quality thresholds, then expand into repeat production cycles with agreed capture, metadata, QC, recapture, and delivery requirements.
Pilot Options
Start Small. Scale With Confidence.
Start with a focused evaluation batch, expand into project-specific production, and continue through repeat delivery cycles as requirements grow.
Option 01
Evaluation Batch
Validate whether a focused real-world scenario exposes meaningful weaknesses in your current evaluation, perception, or training workflow.
- Focused scenario scope
- Structured metadata
- Quality-controlled delivery
- Clear expansion path
Option 02
Custom Collection
Build a field collection project around your deployment environment, target behaviors, capture instructions, metadata, and quality requirements.
- Project-specific capture instructions
- Environment-specific collection
- Flexible delivery structure
- Repeatable collection workflow
Option 03
Ongoing Data Partnership
Continue collecting, validating, and refining real-world data as your model, deployment environment, and engineering roadmap evolve.
- Iterative collection cycles
- Scheduled delivery batches
- Project-dependent capacity expansion
- Long-term data operations
Explore a free sample first, then discuss a pilot tailored to your deployment goals. Scope, timeline, metadata, quality requirements, licensing, and delivery cadence are confirmed before collection begins.
Frequently Asked Questions
Questions Teams Ask Before Starting a Data Project
Practical answers about field operations, custom capture, metadata, quality control, regional production, and delivery.
01 Where can you operate data collection programs?
We currently operate field production capabilities in South Korea and Bangladesh. Additional locations can be evaluated according to target environments, participant requirements, field access, and project feasibility.
02 Can you collect custom egocentric or Physical AI scenarios?
Yes. Collection can be designed around human activities, manipulation tasks, operational workflows, target environments, participant profiles, viewpoints, capture instructions, sensor requirements, and project-defined acceptance criteria.
03 What can be captured besides video?
Depending on the approved capture configuration, records can include IMU, audio, timestamps, GPS, device information, task or route context, capture settings, scene metadata, operational records, and quality indicators linked to each recording.
04 Can the capture protocol and metadata schema be customized?
Yes. Camera configuration, mounting position, recording instructions, required sensor fields, identifiers, metadata fields, naming rules, quality thresholds, and delivery structure can be defined against the buyer's technical requirements.
05 Can we begin with a small calibration or pilot batch?
Yes. A focused pilot can validate field instructions, capture configuration, scenario relevance, metadata structure, technical quality, acceptance criteria, and delivery format before production volume is increased.
06 How do you manage failed captures and recollection?
Captures can be reviewed for technical quality, protocol compliance, metadata completeness, sensor availability, and project-specific rules. Failed records can retain structured rejection reasons and be routed into a replacement or recollection workflow.
07 Can annotation and labeling be included?
Yes. Annotation, labeling, Human Ground Truth, scene attributes, project-specific classes, and other derived fields can be included when they are part of the agreed production and delivery scope.
08 How are privacy, licensing, and controlled delivery handled?
Privacy treatment, anonymization requirements, permitted use, licensing, access controls, retention conditions, integrity verification, and buyer-approved delivery procedures are defined according to each engagement.
Start a Conversation
Turn Your Missing Data Requirement into a Field Production Plan.
Tell us what your model needs to see, where it needs to be captured, and what technical requirements matter. We turn that requirement into a practical collection, validation, and delivery workflow.
A useful first message can include
- Target workflow or missing scenario
- Country, environment, or participant profile
- Video, sensor, or metadata requirements
- Expected volume and timeline
- Quality or acceptance constraints