Image collection
Image datasets for vision models, classification, detection, segmentation and evaluation.
Collection capabilities
Multiple modalities. A project-defined plan.
We scope collection based on intended application and approved sourcing methods. Consent, usage rights and privacy considerations are addressed before production collection.
Video collection
Video capture for activity recognition, tracking, robotics and real-world scene understanding.
Audio collection
Audio collection designed around recording conditions, channel, sample-rate and domain specifications.
Speech collection
Monolingual and multilingual speech recording, including conversational use cases.
Text & multilingual data
Text collection, transcription, translation and language data for NLP and LLM projects.
Conversational data
Human dialogue data with project-specific scripts, metadata and annotation requirements.
Human interaction data
Interaction and egocentric data programs for robotics, embodied AI and behavior research.
Facial image collection
Facial imagery for eligible computer-vision use cases with applicable consent and privacy safeguards.
Healthcare data
Healthcare AI collection scoped to permitted data sources, handling requirements and project purpose.
Custom field collection
Project-specific sourcing plans for in-context data that is not available in existing catalogues.
End-to-end workflow
From requirements to secure delivery.
Collection, annotation and quality assurance are planned together to avoid gaps between raw capture and training-ready data.
01
Requirements
Define requirements
Agree on intended use, modalities, languages, geography, participant profile, metadata and acceptance criteria.
02
Design
Design collection
Develop collection protocols, scripts, recording specifications, consent and data handling plans.
03
Recruitment
Recruit sources
Identify suitable participants or sources using project-approved eligibility and provenance requirements.
04
Capture
Collect data
Run a pilot and production capture against the agreed protocol, with required metadata tracking.
05
Validation
Validate
Check completeness, technical requirements, metadata and protocol adherence.
06
Processing
Annotate & transcribe
Apply agreed annotation, transcription or translation where required by the project.
07
Quality
Quality control
Use human-in-the-loop review, sampling, issue resolution and rework against acceptance criteria.
08
Delivery
Deliver securely
Prepare the validated dataset in the agreed format and transfer it through a project-approved method.
Project planning
Start with your model's data gap.
Share what the system needs to learn, which data is missing and how you plan to use it. We will review sourcing options, collection constraints and a practical QA approach.
- Define the target modality and language
- Set geography and participant criteria
- Agree usage, consent and licensing needs
- Plan annotation, QA and secure handoff
Have existing data?
We can discuss annotation, transcription, translation, validation and data preparation.
Need ready-to-source data?
Explore listed datasets and request specifications or sample availability.
Explore the catalogue →