Image & Video Collection at Scale.
We capture, curate, and structure original image and video datasets globally. Controlled collection protocols, strict metadata discipline, and rigorous human-in-the-loop QA.
Who This Is For
Vision AI Teams
Computer vision engineers training custom detection, OCR, and classification models requiring original asset generation.
Autonomous & ADAS
Robotics and vehicle programs needing diverse, edge-case spatial scenarios sourced globally under strict compliance.
Multimodal AI
Foundation model developers syncing structured visual arrays back to detailed localized text pairings.
Archive Curation
Media organizations digitizing and categorizing massive unstructured visual catalogs into searchable databases.
Scenarios, Hardware, & Metadata.
Executing complex environmental captures guided by rigid framing policies and device standardization requirements.
Controlled Collection
- Specific lighting and staging constraints
- Geographically diverse crowd-sourcing
- In-studio professional actor recording
- Device-specific captures (IoT, mobile, 4K)
Video & Spatial Scenarios
- Sequential action and gesture recording
- Sign language and multimodal interaction
- Long-format environmental surveillance
- Simulated edge-case collision tracking
Annotation-Readiness
- Rich EXIF and structural metadata tagging
- Frame extraction from high-speed video
- Bounding box and polygonal masking prep
- Blurring faces, plates, and restricted identifiers
How It Works
A structured, auditable process designed for enterprise scale.
Protocol Definition
Protocol Definition
Mapping resolution specs, file formats, demographic variety targets, and capture staging rules.
Contributor Routing
Contributor Routing
Assigning tasks to field teams with appropriate hardware, confirming consent architectures.
Native Capture
Native Capture
Assets recorded via controlled endpoints, uploading raw files with location and timestamp metadata.
Visual Quality Control
Visual Quality Control
Reviewers inspect files against protocol - rejecting blur, poor framing, incorrect lighting.
Secure Handoff
Secure Handoff
PII scrubbed, metadata serialized, bundle synced to your storage.
Global Scenario Diversity.
Vision models trained solely on stock photography or limited Western geographies fail in the real world. You need authentic, culturally diverse background data to build robust spatial awareness.
Visual QA Gates
A dataset of 100,000 images is useless if the resolution drops or the metadata is misaligned. Our QA loops verify technical specs and semantic correctness.
- Technical Auditing: Automated validation of target resolutions, frame rates (FPS), orientation bounds, and color bit-depth.
- Semantic Profiling: Human-in-the-loop review confirming the subject matter genuinely matches the requested scenario prompt.
- Authenticity Verification: Detecting AI-generated injects, deepfakes, or tampered metadata uploaded by crowd participants.
Production Format Types
Related Programs
Explore how we deliver dataset operations at global scale.
Service FAQ
Common operational and scoping questions regarding this specific pipeline.
We require digitally validated, GDPR and CCPA compliant waiver signatures from every participant before they can initiate a recording task. Full audit trails of these agreements are retained.
Yes. Our collection endpoint apps record secure EXIF injection and localized telemetry. Our L1 QA teams also utilize detection scanning software to rip out synthetic imagery before it enters your batch.
Yes. While this capability focuses on the collection phase, our annotation delivery teams can subsequently execute full polygonal masking and object tracking on the generated assets.
Tasks that fail the staging protocol check during QA are rejected instantly, and the system prompts a new capture event from the contributor pool until the exact resolution and framing parameters are satisfied.
Map Your Vision Collection Needs
Share your asset targets, scenarios, and metadata constraints. We'll deploy the execution workforce.