Specification-ledRequirements and acceptance criteria defined first.
REAL-WORLD DATA • HEALTHCARE DATA • ENTERPRISE AI DATA
Real-world, healthcare and enterprise AI data.
Custom real-world collection, healthcare datasets and enterprise AI data products—supported by annotation, metadata, human QA and governed delivery.
AMSYNK DATA PLATFORMS
Data infrastructure built for real-world AI deployment.
Custom collection, qualified healthcare and enterprise datasets, source and rights review, human QA, annotation, metadata and governed delivery across operating regions.
01
DATA OPERATIONS
AMS-RWD
Real-World Data Operations
Custom multimodal collection executed against defined technical specifications, acceptance criteria and delivery requirements.
CaptureEgocentric · Ego-Exo · Multi-Cam
ModalitiesVideo · Audio · Image · Document
ExecutionPilot-led field operations
02
HEALTHCARE DATA
AMS-MED
Healthcare Data Catalogue
Medical imaging, clinical, longitudinal, oncology, specialty and synthetic healthcare products for qualified evaluation.
Data familiesImaging · Clinical · Longitudinal
SpecialtyOncology · Cardiac · Dental · Audio
AccessQualification before commitment
03
ENTERPRISE AI DATA
AMS-ENT
Enterprise AI Data Catalogue
Training corpora, agent environments, curated programs, software/code assets and engineering data products.
CorporaLogs · Service Desk · Conversations
Technical dataCode · CAD/STEP · Sensor data
Use casesTraining · Evaluation · Agents
Pilot before scaleCapture setup and sample quality validated early.
Batch-level controlReview points, exceptions and corrections stay traceable.
Documented handoffAccepted files, metadata and manifests organized clearly.
Real-world data operations
Operational depth for data that must be captured in the real world
When existing data is not the right answer, AMSYNK can translate a specification into controlled video, audio, image, document and multimodal collection workflows.
First-person workflows
Capture human task activity from the participant’s viewpoint
Head-mounted, wrist-mounted, or supported smart-camera setups record hands, tools, object interactions, and task sequence in real environments.
- Capture methods
- Head-mounted • wrist-mounted • supported smart cameras
- Control points
- Camera angle • task visibility • consent • protocol adherence
- Typical outputs
- Video • timestamps • task metadata • delivery manifests
Synchronized viewpoints
Add external context to first-person task capture
Fixed cameras complement the participant view with wider task, body-position, environment, and workflow context.
- Capture methods
- First-person view • side view • wide environment view
- Control points
- Clock alignment • framing • occlusion • file pairing
- Typical outputs
- Synchronized video sets • camera map • timing notes • manifests
Speech and conversational data
Record language data against defined speaker and audio specifications
Programs can cover scripted speech, paired conversation, call audio, multilingual recording, and transcription-ready assets.
- Collection inputs
- Language • speaker profile • script or topic • device requirements
- Control points
- Audio format • noise level • speaker match • duplicate review
- Typical outputs
- Audio files • transcripts • speaker metadata • QC summaries
Text and visual language data
Collect approved documents and scene text from real environments
Workflows can cover printed, handwritten, form-based, market, office, and public-facing text sources according to the agreed scope.
- Collection inputs
- Language • document type • geography • image requirements
- Control points
- Permission • readability • duplication • category balance
- Typical outputs
- Images • source metadata • category labels • manifests
Structure and validation
Turn recordings into reviewable, specification-aligned datasets
Annotation and QA can include task boundaries, events, objects, timestamps, metadata completion, file checks, and batch-level review.
- Inputs
- Taxonomy • schema • examples • acceptance rules
- Control points
- Label consistency • missing fields • file integrity • sample audits
- Typical outputs
- Annotations • metadata tables • exception logs • QC reports
Pre-procurement review
Evaluate dataset suitability before a commercial decision
Available datasets can be reviewed for sample quality, format, metadata, duplication, documentation, licensing scope, and requirement fit.
- Review inputs
- Samples • specifications • metadata • documentation
- Control points
- Language or domain fit • technical format • quality risks • rights scope
- Typical outputs
- Evaluation notes • risk summary • suitability recommendation
Multimodal capture systems
One task, multiple viewpoints, complete operational context
Collection design connects the participant view, external cameras, environment conditions, and task metadata instead of treating them as separate services.
01 First-person view
02 External task view
03 Environment context
04 Wrist view
Synchronized capture design
View capture capability
Viewpoints are planned around what the model needs to observe
Camera position, task framing, participant movement, tools, environment constraints, and required metadata are confirmed through the specification and pilot.
Participant viewHands, tools, objects and sequence.
External viewsBody position, wider action and context.
Task protocolRepeatable instructions and acceptance rules.
Structured outputPaired files, timing notes and manifests.
Skilled & maintenanceRepair • tools • assembly
Household activityDaily routines • object interaction
Industrial & fieldOperations • agriculture • workflows
ManufacturingLogisticsAgricultureHealthcare workflowsRetail operationsHospitalitySmart environmentsHousehold activities
Existing data & AI data products
When collection is not the right answer, start with a qualified catalogue.
Healthcare procurement and enterprise AI data are separated into dedicated marketplaces so technical teams can evaluate the right inventory without navigating unrelated products.
HEALTHCARE DATA MARKETPLACE
Medical imaging, clinical, longitudinal, oncology & synthetic healthcare data
Real-world datasets and clearly classified synthetic healthcare offerings, with technical qualification before commercial commitment.
- CT / MRI / X-ray / cardiac
- Longitudinal & linked EMR
- Oncology & specialty data
- Synthetic healthcare programs
Training corpora, agent environments, code, engineering & curated data
A focused portfolio for pre-training, post-training, enterprise agents, evaluation, reasoning and technical AI programs.
- Enterprise operational / IT logs
- Customer support & service desk
- Curated / hybrid AI data
- Software, CAD / STEP & machine data
Enterprise corpusOperational / IT Log CorpusProduction-scale operational data with causal context and ground truth.
Conversational corpusCustomer Support Chat CorpusMulti-turn support interactions for model and agent training.
HealthcareLinked Imaging + Longitudinal EMRPatient-linked imaging, reports and longitudinal clinical records.
Choose the right starting point
Evaluate existing data, validate a pilot, or move an approved specification into execution
The right path depends on whether the data already exists, the setup needs validation, or the requirement is ready for structured production.
Confirm whether available data is suitable before procurement
Review sample quality, technical format, metadata, documentation, duplication risk, licensing scope, and alignment with the target use case.
- Samples and specifications
- Metadata and documentation
- Quality and suitability risks
Validate tasks, devices, viewpoints, instructions, and acceptance criteria
A representative pilot exposes practical issues before scale and provides a clear basis for correction, approval, and production planning.
- Task and participant feasibility
- Camera setup and capture protocol
- Sample review and corrective actions
Move an agreed specification into controlled production batches
Execution is planned around recruitment or sourcing, field operations, batch review, exception handling, metadata completion, and structured delivery.
- Production and batch planning
- Quality checkpoints and issue handling
- Accepted files, metadata and manifests
From scope to delivery
A controlled path from scope to delivery
Five defined stages create clear review points from the first requirement through final handoff.
-
Requirement definition
Define the use case, modality, volume, timeline, permissions, and acceptance criteria.
Agreed scope -
Protocol and pilot
Translate the specification into instructions, capture setup, and a representative validated sample.
Validated setup -
Controlled collection
Execute the agreed sourcing or capture workflow with documented batch controls.
Controlled batches -
QA and structuring
Review file integrity, task visibility, metadata, and the agreed annotation schema.
Accepted dataset -
Structured delivery
Organize accepted files, manifests, documentation, and the agreed final handoff.
Documented handoff
Quality & data governance
Trust is built into the operating path—not added at final delivery.
AMSYNK structures each engagement around the evidence and controls appropriate to the data type: specification, provenance, permitted use, privacy handling, quality gates, transfer and acceptance.
Review governance frameworkSpecification & acceptance
Units, formats, labels, metadata, rejection logic and final handoff defined against the project.
Source, rights & provenance
Ownership or licensing position and relevant source evidence qualified where applicable.
Privacy & sensitive data
Project-specific participant permissions, de-identification, access and handling requirements.
Quality gates & delivery
Pilot review, batch checks, metadata reconciliation, transfer and acceptance documentation.
Frequently asked questions
Questions buyers ask before scoping
Does AMSYNK support both new collection and existing datasets?
Yes. AMSYNK supports custom real-world collection as well as qualified healthcare and enterprise AI data products. The route depends on whether the requirement needs new capture, an existing dataset, or a curated/custom data program.
How are marketplace datasets and data products qualified?
Public listings are used for technical discovery. Availability, format, source or generation position, rights, permitted use, evaluation access and final delivery scope are confirmed for the specific engagement before commitment.
Can a project include annotation, metadata and QA?
Yes. Depending on the specification, AMSYNK can coordinate structured labels, temporal or object annotations, metadata completion, file-level checks, batch QA and delivery manifests.
Do projects move directly to full scale?
The preferred path is requirement qualification, representative evaluation or pilot, feedback and correction, then controlled production or delivery against agreed acceptance criteria.
Specialized capabilities
Go deeper by data type, workflow or procurement path
Use specialist pages when the requirement needs more technical depth than the company overview.
Healthcare Data MarketplaceMedical imaging, longitudinal, clinical and specialty healthcare datasetsEnterprise AI Data MarketplaceEnterprise corpora, synthetic, curated, engineering and agent-training data productsEgocentric & Ego-Exo VideoFirst-person and synchronized multi-view task captureRobotics & Embodied AI DataHuman demonstrations and multi-view real-world task dataSpeech & Audio DataProject-defined speech and audio recording workflowsDocument & Image DataReal-world document, image and photo collectionAnnotation, Metadata & QAStructured labels, metadata and acceptance controlsOTS Dataset SourcingExisting dataset evaluation, rights review and technical fit
Start with the requirement
Share the technical scope. AMSYNK will map the right data path.
Custom collection, healthcare datasets, enterprise corpora, annotation, QA or governed delivery can start from one technical brief.
Data typeGeography / languageVolumeFormat / metadataIntended use