Hasty Alternatives: Annotation Tool vs Physical AI Data
Last updated: March 31, 2026. If anything here is inaccurate, email [email protected].
TL;DR
- Hasty is an AI-assisted annotation tool integrated into CloudFactory's AI Data Platform.
- The tool emphasizes smart suggestions and feedback loops to accelerate labeling.
- Hasty claims AI-assisted annotation can reduce labeling time by up to 30x.
- Supported annotation types include semantic segmentation, object detection, and instance segmentation.
- Quality workflows include 100% QA, consensus scoring, and automated quality control.
- Hasty highlights no-code model development to train custom models on labeled data.
- Claru is purpose-built for physical AI capture and multi-layer enrichment.
- Choose Hasty for annotation tooling; choose Claru for capture + enrichment of robotics data.
What Hasty Is Built For
Key differences in 60 seconds: Hasty provides AI-assisted annotation tooling. Claru is a capture-and-enrichment pipeline for physical AI training data.
Hasty is described as an AI-assisted annotation tool integrated into CloudFactory's AI Data Platform. [1]
The tool emphasizes smart suggestions and feedback loops for faster labeling. [2]
CloudFactory claims Hasty can reduce labeling time by up to 30x through AI-assisted annotation. [3]
Supported annotation types include semantic segmentation, object detection, and instance segmentation.[4]
Quality workflows include 100% QA, consensus scoring, and automated quality control. [5]
The platform highlights no-code model development for training custom models on labeled data. [6]
CloudFactory also emphasizes data security protocols and compliance support. [7]
Hasty was originally an independent AI-assisted annotation startup before being acquired by CloudFactory, a managed workforce platform for AI data operations. The acquisition combined Hasty's annotation tooling with CloudFactory's global workforce of managed labelers, creating an integrated platform that offers both the software and the human resources for computer vision labeling. This integration positions Hasty as more than a standalone tool: it is now part of a full-service data operations offering that handles annotation from task design through delivery.
For physical AI and robotics teams, the question is whether annotation tooling and managed labeling address the core data bottleneck. Robotics models built on imitation learning, diffusion policies, or vision-language-action architectures need training data with specific properties: egocentric viewpoints, manipulation context, depth alignment, and action-level temporal annotations. These requirements demand specialized capture infrastructure before annotation can even begin. Hasty excels at the annotation layer, but the upstream challenge of physical-world data acquisition for robotics remains outside its scope.
If your bottleneck is annotation tooling and QA, Hasty is a strong fit. If your bottleneck is physical-world capture and enrichment, Claru is the better fit.
Company Snapshot
- Focus
- AI-assisted annotation tool within CloudFactory's platform.[1]
- Speed
- Claims up to 30x faster labeling via AI assistance.[3]
- Annotation types
- Semantic segmentation, object detection, instance segmentation.[4]
- Quality
- 100% QA, consensus scoring, automated quality control.[5]
- Best fit
- Teams needing AI-assisted annotation tooling
- 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
- Teams that need capture + enrichment for embodied AI
Key Claims (With Sources)
- Hasty is an AI-assisted annotation tool integrated into CloudFactory's AI Data Platform. [1]
- Hasty emphasizes smart suggestions and feedback loops for faster labeling. [2]
- Hasty claims AI-assisted annotation can reduce labeling time by up to 30x. [3]
- Annotation types include semantic segmentation, object detection, and instance segmentation. [4]
- Quality workflows include 100% QA, consensus scoring, and automated quality control. [5]
- The platform highlights no-code model development and data security protocols. [6][7]
Where Hasty Is Strong
AI-assisted labeling
Hasty highlights smart suggestions and feedback loops.[2]
Speed claims
CloudFactory claims labeling time can drop by up to 30x.[3]
Annotation breadth
The tool supports segmentation and detection workflows.[4]
Automated QA
Quality workflows include 100% QA, consensus scoring, and automated quality control. [5]
No-code model training
Hasty highlights no-code model development for training custom models on labeled data. [6]
Where Claru Is Different
Capture-first
Claru starts by capturing physical-world data instead of focusing only on annotation tooling.
Enrichment layers
Depth, pose, and motion signals are generated as first-class outputs.
Robotics-ready delivery
Claru ships datasets in formats that plug directly into robotics stacks.
Task-specific collection
Claru designs capture briefs around real robot behaviors and environments.
Hasty vs Claru: Side-by-Side Comparison
| Dimension | Hasty | Claru |
|---|---|---|
| Primary focus | AI-assisted annotation tooling within CloudFactory's platform.[1] | Physical AI training data for robotics and world models |
| Speed claims | AI-assisted labeling claims up to 30x faster annotation.[3] | Capture + enrichment optimized for robotics timelines |
| Annotation types | Semantic segmentation, object detection, instance segmentation.[4] | Expert annotation plus enrichment outputs |
| Quality workflows | 100% QA, consensus scoring, and automated quality control.[5] | Multi-layer enrichment and expert QA |
| Model development | No-code model development for training custom models.[6] | Datasets delivered ready for robotics training pipelines |
| Data capture | Annotation tooling for existing data | Collector network plus task-specific capture |
| Enrichment | Annotation outputs and QA | Depth, pose, segmentation, optical flow, aligned captions |
| Best fit | Teams needing AI-assisted annotation tooling | Teams needing capture + enrichment for physical AI |
Deep Dive: Hasty vs Claru
Hasty focuses on AI-assisted annotation tooling. Claru focuses on capture and enrichment for physical AI.
Tooling vs pipeline
Hasty provides AI-assisted annotation within a platform workflow.
Claru delivers capture, enrichment, and training-ready datasets.
Quality and QA
Hasty highlights 100% QA, consensus scoring, and automated quality control.
Claru pairs expert QA with enrichment outputs like depth and pose.
Model development
Hasty includes no-code model development for custom models.
Claru focuses on delivering datasets ready for robotics training.
Robotics AI data challenges
Modern robotics AI systems require training data that goes beyond what annotation tooling alone can produce. Policy learning architectures need demonstrations captured from viewpoints that match robot camera placements. Manipulation models require hand-object interaction sequences with spatial context. World models need diverse environment recordings with consistent depth and motion information. These requirements demand specialized capture infrastructure deployed in real-world settings.
Claru addresses these upstream requirements by designing capture protocols around robotics tasks, deploying collectors with wearable cameras, and enriching every clip with depth estimation, pose detection, segmentation, and optical flow before delivery in formats that plug directly into robotics training frameworks.
Where each wins
Hasty is strong when annotation tooling is the bottleneck, particularly for computer vision teams that have existing image or video data and need AI-assisted labeling with automated quality control to accelerate their annotation pipelines.
Claru is stronger when physical-world capture is the bottleneck, especially for robotics teams that need new task-specific data with multi-layer enrichment as a standard output rather than annotation of existing datasets.
When Hasty Is a Fit
- You need AI-assisted annotation tools for CV tasks.
- You want smart suggestions and feedback loops to speed labeling.
- You need automated QA and consensus scoring workflows.
- You want no-code model development alongside labeling.
When Claru Is a Fit
- You need physical-world data captured for robotics tasks.
- You want enrichment layers like depth, pose, and motion signals.
- You need datasets delivered in robotics-native formats.
- You want task-specific capture briefs for real-world behaviors.
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
Choose Hasty when you need AI-assisted annotation tooling with strong QA workflows.
Choose Claru when you need capture and enrichment of physical-world data for robotics training.
Some teams use both: Hasty for annotation tooling, Claru for capture-first datasets.
If your project requires custom capture and enrichment, prioritize providers built for physical data collection.
Frequently Asked Questions
What is Hasty?
Hasty is an AI-assisted annotation tool now integrated into CloudFactory's AI Data Platform following its acquisition. [1] Originally an independent startup, Hasty built its reputation on using AI models to assist human annotators with smart suggestions and feedback loops. The CloudFactory acquisition combined Hasty's annotation technology with a managed global workforce, creating a full-service platform for computer vision data labeling that pairs tooling with human labeling capacity.
How does Hasty speed up labeling?
Hasty emphasizes smart suggestions and feedback loops that learn from annotator corrections to provide increasingly accurate pre-labels. [2] As annotators work through datasets, the AI model behind Hasty improves its suggestions, reducing the manual effort required for each subsequent label. This active learning approach is designed to accelerate annotation velocity while maintaining quality, making it particularly effective for large computer vision datasets where similar patterns repeat across images.
Does Hasty claim faster labeling?
CloudFactory claims AI-assisted annotation through Hasty can reduce labeling time by up to 30x compared to fully manual annotation. [3] This speed improvement comes from the combination of AI pre-labeling, smart suggestions, and active learning feedback loops. The actual speedup depends on the complexity of the annotation task, the quality of the initial model, and the consistency of the data being labeled. Simpler tasks with repetitive patterns see the largest time reductions.
What annotation types does Hasty support?
The tool supports semantic segmentation, object detection, and instance segmentation as its primary annotation types. [4] These annotation types cover the most common computer vision labeling needs, from classifying pixels in medical images to drawing bounding boxes around vehicles in driving data. For robotics-specific annotation needs like affordance labeling, grasp type classification, or action boundary marking, teams may need specialized tooling beyond standard CV annotation types.
What QA workflows does Hasty offer?
Hasty highlights 100% QA, consensus scoring, and automated quality control as part of its annotation pipeline. [5] The 100% QA feature means every annotation passes through a review step. Consensus scoring compares labels from multiple annotators to identify disagreements. Automated quality control uses statistical methods to flag potential errors. These workflows are designed to maintain high label accuracy across large annotation projects.
Does Hasty include model training?
Hasty highlights no-code model development for training custom models on labeled data. [6] This feature allows teams to train basic computer vision models directly within the platform using their annotated datasets, without writing code. The no-code approach lowers the barrier to model experimentation, though production robotics teams typically use specialized training frameworks and architectures that require custom training pipelines.
When is Claru a better fit?
Claru is a better fit when you need capture, enrichment, and delivery of robotics-ready datasets. Hasty excels at annotation tooling for existing data, but robotics teams often face an upstream bottleneck: they need new physical-world data collected for specific tasks. Claru provides capture infrastructure, trained collectors with wearable cameras, and enrichment layers including depth, pose, segmentation, and optical flow, all delivered in robotics-native formats like RLDS and WebDataset.
Can teams use both Hasty and Claru?
Some teams use Hasty for annotation tooling and Claru for capture-first physical AI datasets. This combination works well when a team has both existing data that needs labeling and new data collection requirements for robotics tasks. Claru handles the capture and enrichment pipeline for physical-world data, while Hasty provides the annotation interface for labeling tasks where AI-assisted tooling accelerates the process.
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.