AI training data

AI data services organized by modality and delivery need

Custom collection, multi-view capture, annotation, metadata, quality review, and documented dataset evaluation—scoped to the target use case.

Egocentric Exocentric Annotation Robotics & Embodied AI Audio Text & Documents OTS Datasets

Egocentric data collection

Capture human task activity from the participant’s point of view

Wearable or head-mounted cameras record hands, tools, objects, and task sequences from a first-person perspective. Projects can cover household activities, industrial work, agriculture, maintenance, assembly, and other approved environments.

First-person video Wearable camera capture Human activity data Task sequence capture Optional synchronized audio Client-defined metadata

Agriculture egocentric first-person collection

Exocentric & multi-camera collection

Add task context through controlled external-camera views

Fixed or tripod-mounted cameras provide third-person context around the participant. When synchronized with the wearable camera, they help clients review hand movement, body posture, environment, and task progression.

Fixed camera angles Tripod-based capture Multi-view task context Wearable + external sync Environment visibility Capture protocol control

Video annotation & metadata

Turn raw recordings into structured, reviewable datasets

Annotation scope depends on the client schema. Support may include activity boundaries, event tags, timestamps, object references, task outcomes, file manifests, and structured metadata.

Task segmentation Timestamping Activity labels Object references Metadata schemas Delivery manifests

Video annotation and task review workstation

Robotics & embodied AI data

Structure human demonstrations for embodied AI workflows

Projects can combine task video, synchronized viewpoints, timestamps, sensor or device metadata, task labels, and delivery structures designed for perception, planning, and manipulation pipelines.

Human task demonstrationsSynchronized viewpointsTimestamp alignmentSensor-aware metadataTask and outcome labelsPipeline-ready packaging

Structured human-task demonstrations and quality review for embodied AI data

Audio data collection

Speech and conversational audio for language and voice systems

Audio programs can include headset recording, call-center style conversations, prompted speech, paired recording, multilingual collection, and audio review workflows.

Headset recording Prompted speech Conversation data Call-center audio Multilingual collection Audio QA

Headset audio recording and review workstation

Text, image & document data

Collect text and document data from approved real-world sources

Projects may include handwritten or printed documents, scene text, forms, receipts, signs, product packaging, and other client-approved visual or textual data.

Printed documents Handwritten samples Scene text Forms & receipts Image metadata Language coverage

Document and image data collection in a retail environment

OTS datasets & sample evaluation

Evaluate dataset suitability before procurement

Amsynk coordinates sample access, format checks, documentation review, metadata assessment, licensing questions, and technical feedback so the dataset can be evaluated against the intended use case.

Sample access Technical format review Metadata assessment Documentation review Licensing clarification Evaluation handoff

Audio dataset sample evaluation and quality review

Delivery outputs

Defined deliverables—not just raw files

Each engagement is scoped around the model objective, capture protocol, acceptance criteria, metadata structure, and delivery format.

Capture assets

Original recordings or images organized by task, participant, environment, device, session, and collection batch.

Metadata package

Project-defined fields such as task ID, timestamps, viewpoint, device details, language, environment, and review status.

Quality records

Sample-review notes, batch checks, issue tracking, rejection reasons, and delivery reconciliation aligned with the agreed specification.

Delivery structure

Clearly named folders, manifests, checksums where required, and agreed formats prepared for secure transfer or client ingestion.

Project questions

AI data collection FAQs

What information is needed to assess a collection project?

Share the data modality, geography, target participants or environments, estimated volume, devices, task instructions, metadata requirements, timeline and acceptance criteria.

Can egocentric and exocentric cameras be used together?

Yes. A project can combine wearable first-person capture with fixed or operator-positioned external cameras when synchronized multi-view context is required.

Do you support pilot collections before scaling?

Yes. A pilot is used to validate instructions, task visibility, device placement, file structure, metadata and QA expectations before larger production cycles.

What can be included in the final delivery?

Depending on the project, delivery may include raw media, approved annotations, metadata tables, task notes, quality logs and an agreed folder and naming structure.

Dataset Due Diligence

OTS evaluation before commercial use

Existing datasets are assessed against the intended use case and available documentation. Evaluation does not imply automatic approval.

Provenance

Review the stated source, collection context, ownership chain and available supporting records.

Rights and licensing

Inspect permitted uses, transfer rights, restrictions and documentation relevant to the proposed project.

Technical sample

Check format, duration, duplication, signal or image quality, metadata coverage and sample consistency.

Project fit

Compare the dataset against language, domain, geography, task, demographic and acceptance requirements.

Have a defined data requirement?

Share the modality, geography, volume, timeline, device setup, metadata, and acceptance criteria.

Submit requirement