Aya Data Alternatives: Data Annotation vs Physical AI Capture
Last updated: March 31, 2026. If anything here is inaccurate, email [email protected].
TL;DR
- Aya Data provides data annotation and data collection services across multiple modalities.
- Aya Data positions itself as an end-to-end data partner for AI teams.
- Claru is purpose-built for physical AI data capture and enrichment.
- Choose Aya Data when you need general annotation or collection services.
- Choose Claru when you need robotics-ready datasets captured from the physical world.
What Aya Data Is Built For
Key differences in 60 seconds: Aya Data is a broad AI data services provider focused on annotation and collection. Claru is a physical AI pipeline focused on capture and enrichment for robotics.
Aya Data highlights data annotation services across major data types and industries, along with data collection services. [1]
The company also positions itself as an end-to-end AI data partner with consulting and delivery support. [2]
If your bottleneck is general data annotation or collection, Aya Data can help. If your bottleneck is physical-world capture and robotics enrichment, you need a specialized pipeline.
Company Snapshot
- Focus
- Data annotation and data collection services. [1]
- Coverage
- Multi-modal annotation and collection
- Core output
- Labeled datasets and collected data for AI training
- Best fit
- Teams needing general annotation or data collection
- Focus
- Physical AI training data for robotics and world models
- Capture
- Wearable camera network plus task-specific collection
- Enrichment
- Depth, pose, segmentation, optical flow, aligned captions
- Best fit
- Robotics teams that need capture + enrichment
Where Aya Data Is Strong
Why Physical AI Teams Evaluate Alternatives
Capture-first pipelines
Physical AI models require real-world data collection with task-specific capture programs.
Enrichment layers
Depth, pose, segmentation, and motion signals are critical for robotics training.
Training-ready delivery
Claru ships datasets in formats that plug directly into robotics stacks.
Aya Data vs Claru: Side-by-Side Comparison
| Dimension | Aya Data | Claru |
|---|---|---|
| Primary focus | Data annotation and data collection services. [1] | Physical AI training data for robotics and world models |
| Core output | Labeled datasets and collected data | Training-ready physical datasets with enrichment layers |
| Data capture | General data collection programs | Collector network plus teleoperation and task-specific capture |
| Enrichment | Annotation layers tailored to client needs | Depth, pose, segmentation, optical flow, aligned captions |
| Best fit | Teams needing broad annotation or data collection | Robotics teams needing capture + enrichment |
Deep Dive: Aya Data vs Claru
Aya Data is a general AI data services provider. Claru is a physical AI data pipeline.
General annotation vs physical capture
Aya Data emphasizes annotation and data collection across modalities.
Claru emphasizes capture and enrichment of real-world physical data for robotics training.
Annotation layers vs enrichment layers
Aya Data delivers labeled datasets based on client schemas.
Claru adds enrichment layers like depth and pose that are core model inputs for robotics.
Where each provider fits
Aya Data is a strong fit for general AI annotation and collection workflows.
Claru is a better fit when the challenge is physical-world capture and robotics-ready delivery.
When Aya Data Is a Fit
- You need general data annotation or collection services.
- You already have data and need labeling support.
- You want a broad AI data services partner.
When Claru Is a Fit
- You need new physical-world data captured for robotics tasks.
- Your model depends on enrichment layers like depth and motion.
- You want datasets delivered in robotics-native formats.
How Claru Delivers Physical AI Data
Claru provides an end-to-end pipeline so physical AI teams can move from brief to training-ready data quickly.
Scope the Dataset
Define the target behaviors, environments, and label schema with your research team. We align on formats, enrichment layers, and success criteria before capture begins.
Capture Real-World Data
Activate the collector network, teleoperation runs, or game-based capture to gather the exact clips your model needs.
Enrich Every Clip
Generate depth maps, pose, segmentation, and optical flow in batch. Cross-validate signals to ensure aligned training inputs.
Expert Annotation
Specialized annotators label action boundaries, affordances, and intent using project-specific guidelines and QA checks.
Deliver Training-Ready
Ship datasets in WebDataset, HDF5, RLDS, or your native format with manifests, checksums, and datasheets.
Claru by the Numbers
Other Alternatives Worth Considering
If you are mapping the data provider landscape, these comparisons cover adjacent options.
How to Choose
If you need broad annotation or collection services, Aya Data is designed for that scope.
If you need capture and enrichment of physical-world data for robotics training, Claru is a better fit.
Some teams use both: Aya Data for labeling, Claru for physical datasets.
Frequently Asked Questions
What is Aya Data?
Aya Data provides data annotation and data collection services for AI teams. [1]
Does Aya Data provide data collection?
Yes. Aya Data highlights data collection services alongside data annotation. [2]
Is Aya Data a physical AI data provider?
Aya Data focuses on general data annotation and collection rather than physical-world capture for robotics.
When is Claru a better fit?
Claru is a better fit when you need capture, enrichment, and delivery of robotics-ready datasets.
Need Physical AI Data That Ships Fast?
Tell us what you are training. We will scope a capture plan and deliver a pilot dataset in days.