We design and operate project-specific data programs from capture architecture and managed field collection through Human GT, multimodal metadata, quality validation, and engineering-ready delivery for Physical AI, robotics, computer vision, and autonomous systems.
Each layer can be scoped independently or combined into a managed end-to-end program spanning capture design, field production, Human GT, metadata, validation, and structured delivery.
Project-specific capture systems, field workflows, environments, participants, sensors, and acceptance criteria are configured around the target model and task.
Automated outputs and collected data can be reviewed, corrected, verified, annotated, and refined for higher-confidence training, validation, and evaluation use.
Sensor metadata, timestamps, synchronization records, validation checks, exception logs, and QC documentation make datasets easier to review, trace, and integrate.
Validated multimodal data is organized and packaged with schemas, manifests, documentation, metadata, QA records, and project-defined delivery formats.
Scope is defined from model requirements, target tasks, capture conditions, sensors, metadata, quality rules, and delivery requirements.
Automated processing reduces repetitive work, while targeted human review corrects missed objects, false positives, inconsistent labels, and difficult scenes before final delivery.
Instead of treating every frame or annotation equally, review can focus on exceptions, difficult scenes, uncertain outputs, and records that directly affect delivery quality.
Find and correct objects or privacy regions that were not detected during automated processing.
Remove incorrect detections, duplicate regions, and unnecessary annotations.
Correct class labels, bounding boxes, alignment, and project-specific annotation rules.
Review flicker, unstable detections, scene transitions, and inconsistencies across video frames.
Delivery can include automated outputs, exception-only review, corrected annotations, human-verified ground truth, or a combination of these stages according to the required quality, cost, and turnaround.
Processed assets, labels, metadata, manifests, and quality records are organized into a structure your engineering team can inspect, version, and connect to downstream training or evaluation workflows.
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Delivery structure is defined from the required framework, annotation schema, source assets, metadata fields, version rules, and intended engineering use.
Project-specific conversion depends on the source schema, target format, required validation, and downstream system.
Real-world video and image data can be processed to reduce exposure of identifiable faces and license plates. Automated detection handles the first pass, while uncertain, missed, or difficult regions can be reviewed and corrected by people.
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Human review remains available for missed regions, uncertain detections, small objects, difficult lighting, occlusion, and frame-to-frame inconsistency.
Detection rules, blur treatment, review depth, and acceptance criteria are defined after representative sample footage has been evaluated.
We do not claim that every submitted file is automatically or completely anonymized without evaluation. Final scope, target objects, review depth, quality criteria, and delivery conditions are confirmed for each project.
Every project begins with a defined requirement and representative sample. Workflow, review depth, quality targets, delivery structure, and production scale are confirmed before larger processing begins.
Frequently Asked Questions
These answers explain how source-data review, human verification, metadata, quality assurance, privacy processing, delivery formats, and pilot scoping are handled.
Origin Data Lab can review video, image, extracted-frame, annotation, and metadata packages. Technical feasibility depends on file format, media quality, dataset size, target objects, and required output.
Yes. Face and license plate anonymization is available through sample-based pilot evaluation. The pilot defines detection rules, blur treatment, human-review depth, and quality criteria.
No. Automated detection is used for the first processing pass, while difficult scenes, missed regions, false positives, and uncertain results may require human review and correction.
Human GT refers to annotations or detection results that have been inspected, corrected, and verified by a human reviewer. Outputs can be marked as automated, human-corrected, or human-verified.
Yes. Human review can focus on key frames, low-confidence detections, missed objects, false positives, temporal inconsistencies, and other exception cases.
Delivery may include COCO, YOLO, JSON, CSV, media files, extracted frames, metadata records, manifests, QA summaries, and project-specific folder structures.
Yes. Depending on the approved scope, delivery may include file metadata, processing status, verification status, exception logs, completeness checks, schema validation, manifests, and QA summaries.
Yes. A limited pilot is recommended before full production to evaluate source quality, processing accuracy, human-review effort, exception patterns, delivery format, and expected turnaround.
Project-specific NDA terms, access conditions, retention rules, source-file handling, delivery controls, and deletion requirements can be discussed before data is transferred.
Pricing and timelines depend on media duration or image volume, source quality, target-object complexity, automated processing performance, human-review depth, output requirements, and quality-assurance scope.
Tell us what needs to be anonymized, verified, corrected, converted, or documented. We’ll review the source data, expected output, quality requirements, and recommend a focused pilot before larger-scale processing begins.
Scope, source format, review depth, security requirements, delivery structure, pricing, and timeline are confirmed before production processing begins.