Multimodal data, collected and annotated together.
Paired image, video, audio, text, and metadata sets for models that need to reason across senses in real-world context — sourced and labeled by one team instead of stitched together from separate vendors.
Cross-modal datasets, grounded in context.
Only capabilities Ayta can currently deliver or is genuinely prepared to pilot are listed below.
Image + video + audio + text + metadata
Multiple modalities captured together in the same session, so they stay aligned to one real-world moment.
Cross-modal datasets
Datasets built for models that need to reason across two or more modalities at once.
Real-world contextual data
Context captured alongside the core data — environment, device, and situational metadata.
3D / LiDAR point-cloud annotation (where supported)
Point-cloud labeling for spatial and depth-aware models, scoped per engagement.
Looking for a single modality instead? See Data Collection or Data Annotation.
From spec to an aligned, cross-modal dataset.
Every multimodal program follows the same operating sequence.
Scope
Which modalities, how they pair, and what context needs to be captured together.
Recruit
Collectors and environments sourced to support simultaneous multi-modal capture.
Train
Capture protocol covers every modality being collected together, not one at a time.
Capture
Image, video, audio, text, and metadata captured in the same session, same context.
Validate
Cross-modal alignment and completeness checked automatically before human review.
QA & deliver
Human review confirms every modality in the set lines up before delivery.
Have a data requirement?
Give us the specification — we'll build the operation around it.
Start a Pilot →