Amsynk AI Data Solutions
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Powering AI with real-world data

Real-World AI Data.
Structured. Traceable.
Project-Ready.

Amsynk AI Data Solutions supports AI teams with structured data sourcing, custom collection, validation, and delivery across audio, egocentric video, multi-camera workflows, field environments, and document-based datasets.

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Structured Traceable Pilot-led Multi-modal
Audio data collection and monitoring with a headset
Audio & Speech Recording and review
Multi-camera egocentric electronics task capture
Multi-Cam Task capture
Egocentric field collection in agriculture
Field Capture Real-world tasks
01 Scope Requirements and acceptance criteria defined first
02 Pilot Capture setup validated before scale
03 Control Batch-level review and exception handling
04 Delivery Structured files, metadata and manifests
Multi-market coordination Coordination across approved geographies, teams, capture environments, and delivery requirements.
Multi-format capability Audio, video, image, document, and metadata workflows aligned to project specifications.
Multi-modal data OTS datasets, custom collection, egocentric, and industrial capture.
Quality first Structured validation, review loops, and delivery discipline.

Our Core Services

Structured data services for AI and machine-learning teams, with clearly defined capture methods, validation steps, metadata, and delivery requirements.

Speech & audio data

Scripted and conversational speech, call audio, paired recordings, transcription-ready assets, and audio quality review.

Image & document data

Controlled collection of images, printed or handwritten documents, scene data, and task-specific visual assets.

Egocentric video collection

First-person task capture using head-mounted or body-worn cameras across household, field, and industrial environments.

Exocentric & multi-camera

External viewpoints, synchronized cameras, and task-context capture for embodied AI and computer-vision programs.

Robotics & embodied AI data

Human-task demonstrations, synchronized observations, sensor-aware packaging, and structured outputs for robotics workflows.

Annotation, metadata & QA

Taxonomy-aligned labeling, metadata preparation, file validation, sample review, and batch-level delivery control.

AI training data capabilities

Capabilities organised by data modality

A clear service structure for first-person video, external multi-camera capture, speech, documents, robotics data, annotation and dataset evaluation.

Egocentric Data Collection

First-person human activity capture using wearable, head-mounted, wrist-mounted, or supported smart-camera setups.

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Exocentric & Multi-Camera

External fixed-angle recordings that provide task context, environment visibility, and synchronized views.

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Video Annotation

Task segmentation, event tagging, object references, timestamps, and client-defined metadata structures.

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Audio Data Collection

Speech, conversation, call-center, headset, and multilingual audio collection programs.

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Text & Document Data

Printed, handwritten, scene-text, document, and language-focused collection workflows.

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OTS Dataset Sourcing

Source evaluation, sample review, documentation checks, and controlled dataset delivery.

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Egocentric + exocentric data collection

First-person and external-camera task data with synchronized context.

Amsynk supports both egocentric capture from the participant’s point of view and exocentric capture from fixed external camera angles. These views can be synchronized to show hands, tools, surroundings, task sequence, and full workflow context.

  • First-person video recording
  • Wearable camera capture
  • External fixed-camera views
  • Multi-camera synchronization
  • Human activity datasets
  • Workplace and household tasks
  • Industrial workflow capture
  • Client-defined metadata planning
Discuss a Project View full capability
Egocentric wearable camera combined with external multi-camera task capture
Egocentric + external camera views
Synchronized first-person and exocentric industrial task workflows
Synchronized multi-view task workflows
Where egocentric data is useful

Industries and environments supported by first-person task capture

Task environments are selected according to project scope, participant access, safety requirements, capture protocol, and required viewpoints.

Robotics & embodied AI Manufacturing Logistics Agriculture Healthcare workflows Hospitality Retail operations Smart environments AR / VR research Autonomous systems Technical maintenance Household activities
Activity data collection

Real-world task environments

Representative household, maintenance, repair, and factory task environments captured according to the client’s required camera setup and protocol.

Skilled Worker

Real-world skilled worker task collection examples

Household Activities

Real-world household activity collection examples

Factory Egocentric

Real-world factory egocentric task collection examples
Engagement pathways

Start with the engagement model that fits the requirement

Projects can begin with an available dataset review, a controlled pilot, or a custom collection program. The scope is defined before commercial and operational commitments are finalized.

A Structured 6-Step Process

From requirement review to structured delivery, each stage is defined around scope, feasibility, validation, and acceptance criteria.

1

Requirement analysis

Clarify the use case, format, volume, timeline, permissions, and acceptance requirements.

2

Planning & protocol

Define the capture method, operating team, pilot scope, and review checkpoints.

3

Data collection

Execute sourcing or fresh collection across approved environments.

4

Annotation, metadata & QA

Check consistency, format, metadata, and acceptance criteria.

5

Robotics & embodied AI data

Attach supporting structure where the project requires it.

6

Secure delivery

Deliver cleanly packaged data with documentation and support.

Built on quality

Quality-led execution with real-world collection environments.

We focus on verifiable process quality: clear instructions, suitable capture setups, sample validation, and structured delivery. This creates clearer project controls, more consistent review, and better delivery traceability for clients and collection partners.

  • Multi-level quality checks
    Internal review before delivery and clear escalation paths.
  • Permission-aware collection practices
    Permission-aware workflows and controlled field execution.
  • Scalable workflows
    From pilot to larger delivery cycles without losing structure.
  • Practical documentation
    Metadata, notes, and delivery organization built around the project.
Learn more about quality
Quality review, equipment checks, documentation, and controlled data collection operations
Frequently asked questions

Clear answers before project scoping

What types of egocentric data can Amsynk support?

First-person recordings, synchronized multi-camera data, household activities, skilled-work tasks, workplace operations, agricultural activities, and industrial task sequences—subject to feasibility, permissions, and client protocol.

What is the difference between egocentric and exocentric capture?

Egocentric capture records from the participant’s viewpoint. Exocentric capture uses external cameras to show the participant, surroundings, and wider task context. Many projects benefit from using both.

Can the project include audio and metadata?

Yes. Depending on the requirement, collection can include synchronized audio, task labels, timestamps, participant or environment metadata, file naming rules, and delivery manifests.

Do you start directly at full scale?

No. The preferred sequence is requirement review, feasibility, sample, controlled pilot, client feedback, correction, and only then scaled execution.

Delivery anatomy

What a project-ready delivery can contain

The exact package is defined in the statement of work. This example shows the level of structure commonly required for a multi-view task collection.

Package component Example contents Purpose
Capture files Head-mounted and external-camera video, organised by session and camera ID Preserves view-level traceability
Session metadata Participant code, task ID, environment, device, timestamps and duration Supports filtering and dataset analysis
Quality records Technical checks, task-completion review, exceptions and rework status Documents batch-level decisions
Delivery manifest File inventory, checksums, version, accepted count and excluded count Enables reconciliation after transfer

Define your AI data requirement

Share the modality, geography, volume, timeline, capture setup, metadata, and acceptance criteria for an initial feasibility review.

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