Surge AI Alternatives for Robotics and Physical AI Training Data
Surge AI pioneered expert-quality RLHF annotation for LLMs — and they are great at it. But training a robot is not like training a chatbot. You cannot annotate manipulation trajectories with the same workforce that rates conversation quality. Physical AI needs domain-specific collectors, enrichment pipelines, and annotators who understand grasp types, not grammar.
Last updated: March 2026. We update this page as both companies evolve. If anything here is inaccurate, email [email protected].
TL;DR
Surge AI excels at expert-quality RLHF annotation for LLMs — curated annotators, strong on NLP and code evaluation, high quality per label. If you are training a language model and need human preference data from vetted experts, Surge is one of the best options available.
Claru does one thing: training data for physical AI. Robots need egocentric video, depth maps, pose estimation, manipulation trajectories, and action boundary annotations — none of which an NLP annotation workforce can produce. We capture real-world video, enrich it computationally, and have annotators trained on grasp types and affordances, not grammar.
Choose Surge AI for expert LLM/NLP annotation and RLHF data. Choose Claru when you are training robots, world models, or embodied AI and need end-to-end physical data with deep enrichment.
Why LLM Annotation Expertise Does Not Transfer to Physical AI
Surge AI built their reputation on a crucial insight: annotation quality matters more than annotation volume. For RLHF, a small number of expert-quality preference labels is more valuable than millions of noisy crowd-sourced ratings. This is correct, and it is why Surge AI is one of the best options for LLM training data.
The same principle applies to physical AI — quality over quantity — but the definition of quality is entirely different. An expert RLHF annotator can reliably judge whether a chatbot response is helpful, harmless, and honest. That same annotator, shown a video of a robotic arm grasping a coffee mug, would struggle to answer: Is this a power grasp or a precision pinch? Where does the “reach” action end and the “grasp” action begin? Is the approach vector appropriate for the object's center of mass? These questions require physical intuition and domain knowledge that text annotation does not develop.
Beyond annotation skills, physical AI introduces entirely new pipeline stages that text-focused annotation services do not operate. Before any human touches the data, it needs to be captured (egocentric video from real environments) and enriched (depth maps, pose estimation, segmentation, optical flow). Surge AI provides neither of these stages. Their pipeline starts at annotation; Claru's pipeline starts at capture.
This is not a criticism of Surge AI — they are excellent at what they do. It is a recognition that NLP annotation and physical AI annotation are different disciplines that require different companies with different infrastructure, different workforces, and different domain expertise.
What Makes Physical AI Annotation Fundamentally Different
Text annotation and physical AI annotation differ in almost every dimension: the data format, the required expertise, the quality metrics, and the upstream infrastructure needed to make annotation possible.
Temporal Precision Over Categorical Agreement
NLP annotation produces categorical judgments: is this response better or worse? Physical AI annotation produces temporal labels: the 'reach' action starts at frame 142 and ends at frame 287, the 'grasp' begins at frame 288. Quality is measured in milliseconds of boundary accuracy, not inter-annotator agreement on categories. Getting these boundaries wrong by even 200ms can teach a robot the wrong timing for contact.
Spatial and Geometric Reasoning
Text annotators reason about language. Physical AI annotators reason about 3D space — which surfaces are graspable, what is the approach vector, where is the object's center of mass, is the gripper orientation compatible with the grasp type. This requires spatial intuition that comes from understanding physical manipulation, not from reading and evaluating text.
Domain-Specific Taxonomies
RLHF annotation uses taxonomies like helpful/harmless/honest or a 1-5 quality scale. Physical AI annotation uses taxonomies from robotics research: grasp types (power, precision, lateral, hook), manipulation primitives (reach, grasp, lift, transport, place, pour, stir), and object affordances (graspable, stackable, pourable, containable). Annotators must learn and reliably apply these domain-specific classification systems.
Multi-Modal Annotation Alignment
NLP annotation operates on text — a single modality. Physical AI annotation must be aligned across multiple modalities simultaneously: RGB video, depth maps, segmentation masks, pose estimates, and action labels must all correspond at the frame level. Annotators need to reference enrichment layers while making labeling decisions, and their annotations must be spatially and temporally consistent with the automated enrichment.
Surge AI vs. Claru: Side-by-Side Comparison
This comparison focuses on what matters for physical AI and robotics teams. For NLP, RLHF, and LLM training data, Surge AI is an excellent choice — the gaps appear when the project involves video, robotics, or embodied AI.
| Dimension | Surge AI | Claru |
|---|---|---|
| Core Focus | Expert annotation for NLP, RLHF, code, and text quality — LLM training data | End-to-end training data for physical AI: capture, enrich, annotate, deliver |
| Data Capture | None — annotation-only service; you must source and provide the data | 10,000+ trained collectors with wearable cameras across 100+ cities; managed teleoperation; game-based capture |
| Enrichment | None — no computational enrichment pipeline for video or images | 6 automated layers: depth maps, pose estimation, segmentation, optical flow, AI captions — all cross-validated |
| Annotation Workforce | Curated expert annotators — vetted for language, reasoning, and code tasks | Expert annotators trained on physical AI: grasp types, affordances, action boundaries, manipulation intent |
| Modalities | Text, code, chat logs, instruction-response pairs — primarily language | Video, depth maps, pose data, segmentation masks, trajectories — physical AI modalities |
| RLHF Capability | Industry-leading for text-based RLHF: preference pairs, reward model training, instruction evaluation | RLHF for video and physical AI: preference ranking of video clips, robot behavior evaluation, world model outputs |
| Delivery Formats | JSON, CSV — standard text annotation exports for NLP pipelines | WebDataset, HDF5, RLDS, Parquet, COCO — robotics-native formats with enrichment side-channels |
| Pricing | Per-task or project-based; premium for expert quality | Project-based; capture + enrichment + annotation bundled; no long-term commitments |
| Best For | LLM training teams that need high-quality text annotation and RLHF data | Robotics, world model, and embodied AI teams that need the full data pipeline |
When Surge AI Is the Right Choice
Surge AI is a strong company that does important work. If your project matches these profiles, they are likely the better choice:
- RLHF for large language models. If you are training or fine-tuning an LLM and need expert human preference labels — response quality, helpfulness, safety ratings — Surge AI's curated workforce is purpose-built for this. Their annotators understand language nuance and can provide the calibrated judgments that reward models need.
- Text and code annotation. Sentiment analysis, named entity recognition, intent classification, code review, instruction evaluation. These are Surge AI's core competencies, and their quality on these tasks consistently exceeds crowd-sourced alternatives.
- Quality over volume for NLP. When you need 10,000 expert-quality preference labels rather than 1 million noisy ones, Surge AI's model — expert annotators, careful curation, quality control — delivers the precision that matters for reward model training.
- Instruction tuning datasets. Building or curating instruction-following datasets for LLM fine-tuning. Surge AI's annotators can evaluate whether model responses follow instructions, identify failure modes, and provide the human feedback that improves model behavior.
- Content quality evaluation. Evaluating and scoring text content for quality, factuality, tone, and style. This is annotation work that requires reading comprehension and critical thinking — skills that Surge AI's expert workforce demonstrably has.
If your annotation needs are primarily text-based and your models are language models, Surge AI is a strong partner. The alternative search makes sense only when your data needs cross into physical AI territory.
When You Need a Physical AI Data Specialist
The transition from text annotation to physical AI data introduces requirements that no NLP annotation service can address. If your project involves any of the following, you need a specialist:
Your model learns from video, not text
Vision-language-action (VLA) models, world models, and robot manipulation policies learn from video sequences — not from text-response pairs. The annotation challenge is spatial and temporal, not linguistic. You need annotators who can identify action boundaries in manipulation sequences, not annotators who can rate chatbot quality.
You need data captured, not just labeled
Physical AI teams often lack the raw training data entirely. You cannot send a text file to Surge AI and get back annotated manipulation trajectories — the video of those manipulation sequences needs to be physically captured in real environments first. This requires a collector network, not an annotation workforce.
Your pipeline needs enrichment layers
Robotics models consume depth maps, pose estimation, segmentation masks, and optical flow as input features for training. These computational enrichment layers must be generated at scale before annotation begins. No text annotation service provides this infrastructure.
Your annotations must align with multi-modal data
Physical AI annotation is not labeling in isolation — annotators must reference depth maps, segmentation overlays, and pose estimates while making labeling decisions. The annotation interface needs to present multiple data modalities simultaneously, which text annotation platforms are not designed to do.
You need robotics-native delivery formats
Your training pipeline expects WebDataset, HDF5, RLDS, or Parquet with aligned enrichment side-channels. Text annotation services deliver JSON or CSV. The format gap requires significant engineering to bridge, and the enrichment layers do not exist in text annotation outputs.
You are training robots or world models
If your end product is a robot policy, a world model, or an embodied AI agent — not a chatbot or a text classifier — you need a data partner whose entire infrastructure is built for physical AI. The data modalities, annotation expertise, enrichment pipelines, and delivery formats are all different.
Claru's Approach: Capture, Enrich, Annotate, Deliver
Where text annotation services operate one stage of the pipeline, Claru operates all four. This is what end-to-end means for physical AI training data.
Capture
Three parallel data acquisition pipelines run continuously. Wearable camera capture deploys 10,000+ trained contributors with GoPro cameras across kitchens, workshops, warehouses, retail environments, and outdoor spaces in 100+ cities worldwide. Managed teleoperation coordinates demonstrations on client-specific robot hardware with trained operators following structured task protocols. Game-based capture uses custom environments that log synchronized video and control inputs at 60 FPS, producing interaction data with perfect action labels. No annotation service — Surge AI or otherwise — provides this capability.
Enrich
Every clip passes through a multi-model enrichment pipeline before human annotation begins. Monocular depth estimation (Depth Anything V2) generates per-frame depth maps. Semantic segmentation (SAM3) labels every pixel with object class and instance identity. Human pose estimation (ViTPose) extracts 2D and 3D joint positions for hand-object interaction analysis. Optical flow computes dense motion fields between consecutive frames. AI-generated captions provide natural language descriptions. All enrichment outputs are cross-validated for physical consistency. These enrichment layers become training inputs for the model — they are not annotation outputs.
Annotate
Expert human annotators — trained specifically on physical AI tasks — add labels that automated systems cannot reliably produce. Action boundary annotation marks discrete actions (reach, grasp, lift, transport, place) with sub-second temporal precision. Object affordance labels identify graspable surfaces, support structures, and obstacles. Grasp type classification follows robotics taxonomies. Intent annotation captures what the person is trying to achieve. Quality scoring flags problematic clips. Every project uses guidelines co-developed with the client's ML team. This is where Surge AI's RLHF expertise does not transfer — the annotation requires physical domain knowledge, not language reasoning.
Deliver
Datasets ship in the formats robotics pipelines consume: WebDataset for streaming training, HDF5 for dense trajectories, RLDS for reinforcement learning, Parquet for metadata queries. Every delivery includes enrichment layers as aligned side-channels, a manifest with checksums, and a datasheet documenting collection methodology, annotator demographics, known limitations, and intended use cases. Data is delivered via S3, GCS, or direct cloud integration. The output is not labels on text — it is a complete, multi-modal training dataset ready for policy training.
Claru by the Numbers
The Multimodal Shift: Why Annotation Services Are Splitting by Modality
The AI training data industry is undergoing a structural shift. In the LLM era (2022-2025), the primary annotation need was text-based: RLHF preference labels, instruction evaluation, content quality scoring. Companies like Surge AI thrived because they matched expert-quality annotators to expert-level linguistic tasks.
The physical AI era — driven by advances like NVIDIA's physical AI platform and benchmarks such as Open X-Embodiment — introduces fundamentally different data requirements. World models need video with temporal structure. Robot policies need manipulation demonstrations with action labels. Embodied agents need egocentric video with spatial annotations. The data itself is different, the annotation expertise is different, and the infrastructure required to produce it is different.
This is not a failure of text annotation services — it is the natural result of AI expanding beyond language. Just as the shift from rule-based systems to neural networks created demand for data labeling companies, the shift from language models to physical AI is creating demand for a new type of data partner: one that captures, enriches, annotates, and delivers multi-modal training data for models that act in the physical world.
The future is likely a portfolio approach: Surge AI for text and RLHF. Claru for physical AI and robotics. Specialized providers for each modality, rather than one generalist for everything. The question is not which service to choose — it is which service for which part of your training data portfolio.
Other Alternatives Worth Considering
Depending on your data needs, these other providers may also be relevant. Each has different strengths.
Scale AI
Enterprise labelingScale AI offers enterprise-scale data annotation with a massive workforce. Unlike Surge AI's quality-first approach, Scale AI optimizes for volume and breadth across NLP, image, video, and autonomous vehicle annotation. Strengths: proven at massive scale, broad modality coverage, strong enterprise tooling. Weaknesses: annotation-only (no capture or enrichment), not specialized for physical AI, expensive enterprise contracts. Best for large-volume annotation projects where you already have the raw data.
See our Scale AI comparison →Labelbox
Annotation platformLabelbox has evolved from an annotation platform into a broad AI data factory. They now offer RLHF data, custom evaluations, an expert network (Alignerr, 1.5M+ workers), and robotics data capture with teleoperation. Strengths: breadth across AI modalities, large expert network, model evaluations. Weaknesses: breadth over depth — expanding into robotics rather than built for it. Best for teams that need one vendor across NLP, image, video, and robotics data.
See our Labelbox comparison →Appen
Crowd labelingAppen is a legacy crowd-sourced annotation provider with a massive global workforce. Strengths: linguistic diversity, global reach, broad task coverage. Weaknesses: quality has declined in recent years, no physical AI specialization, annotation-only model. Best for high-volume, cost-sensitive NLP and image labeling where perfect quality is less critical than coverage and scale.
See our Appen comparison →Luel (YC W26)
Data marketplaceLuel is a two-sided marketplace for rights-cleared multimodal data. Unlike annotation services, Luel provides the raw data itself. Strengths: fast access to licensed video and image content, rights-cleared for training. Weaknesses: no enrichment pipeline, no annotation service, raw data only. Best for teams that need licensed footage for video generation models and will handle enrichment and annotation in-house.
See our Luel comparison →How to Choose the Right Data Partner for Your Modality
The decision tree is straightforward once you identify your primary data modality and pipeline needs.
If your model consumes text: Surge AI for expert RLHF and quality annotation. Scale AI for high-volume NLP labeling. Appen for cost-sensitive multilingual annotation. These are all strong choices for language-focused AI.
If your model consumes images (not video): Scale AI or Labelbox for standard annotation (bounding boxes, segmentation, classification). V7 for auto-labeling-heavy workflows. These platforms are mature for static image tasks.
If your model consumes video with physical structure: Claru for end-to-end capture, enrichment, and annotation. This is where the gap between text/image annotation services and physical AI data services becomes too wide to bridge. You need a partner that captures real-world video, computes depth and pose and segmentation, and annotates with physical AI domain expertise.
Many teams use multiple providers. Surge AI for RLHF on their language model components. Claru for robotics and embodied AI data. The question is not “which one provider for everything” but “which provider for each data modality in your training stack.”
Related Solutions and Resources
Frequently Asked Questions
What is the main difference between Surge AI and Claru?
Surge AI is an expert annotation service focused on NLP and RLHF tasks for large language models. They provide high-quality human annotation through a curated workforce of vetted annotators who rate chatbot responses, evaluate text quality, label sentiment, and provide preference data for RLHF training. Claru is a vertically integrated training data service for physical AI — we capture real-world video, enrich it with depth maps, pose estimation, and segmentation, and have expert annotators label action boundaries, grasp affordances, and manipulation intent. The core difference: Surge AI annotates text data for LLMs; Claru captures and annotates video data for robots, world models, and embodied AI.
Can Surge AI annotate robotics or physical AI training data?
Surge AI's workforce is primarily trained on text-based annotation tasks: RLHF preference labeling, sentiment analysis, text classification, code review, and instruction evaluation. While their annotators are experts in language and reasoning tasks, physical AI annotation requires a fundamentally different skill set. Annotating manipulation trajectories requires understanding grasp types (power grasp, precision pinch, lateral pinch), action boundary detection with sub-second temporal precision, object affordance labeling (which surfaces are graspable, which are support structures), and spatial reasoning about 3D workspace layouts. These are not skills that transfer from text annotation. Claru's annotators are specifically trained on physical AI tasks and follow project-specific guidelines developed with each client's ML team.
Does Surge AI provide data capture or video enrichment?
No. Surge AI is an annotation-only service. They do not capture video, do not operate a camera network, and do not provide computational enrichment layers like depth maps, pose estimation, segmentation masks, or optical flow. To use Surge AI for any video annotation task, you would need to source and collect the raw video yourself, run your own enrichment pipeline, and then send the data to Surge AI for labeling. Claru handles the entire pipeline — from deploying 10,000+ trained collectors with wearable cameras across 100+ cities, through automated multi-model enrichment (Depth Anything V2, ViTPose, SAM3), to expert human annotation of physical AI-specific labels.
Is Surge AI good for RLHF annotation?
Yes. Surge AI is one of the best options for RLHF annotation on text-based AI systems. Their curated workforce of expert annotators produces high-quality preference ratings, instruction evaluations, and reward model training data for LLMs. If your project involves training or fine-tuning a large language model and you need expert human feedback on text outputs, Surge AI is a strong choice. The limitation appears when teams try to apply the same annotation approach to physical AI — rating robot manipulation sequences requires different expertise than rating chatbot responses.
When should I choose Surge AI over Claru?
Choose Surge AI when your project involves NLP annotation (sentiment, intent, entity extraction), RLHF preference labeling for large language models, code quality evaluation and annotation, text classification or content quality rating, or instruction-following evaluation for chatbots. Choose Claru when your project involves robotics training data (egocentric video, manipulation, teleoperation), world model training (video with depth, segmentation, and temporal structure), physical AI annotation (grasp types, affordances, action boundaries), or any use case that requires data capture and enrichment before annotation.
How do annotation quality standards differ between NLP and physical AI?
NLP annotation quality is typically measured by inter-annotator agreement on discrete labels: does annotator A agree with annotator B that response X is better than response Y? The quality framework revolves around consistency, calibration, and bias detection in categorical judgments. Physical AI annotation quality involves entirely different metrics: temporal precision of action boundaries (measured in milliseconds), spatial accuracy of affordance labels (measured in pixels), consistency of grasp type taxonomies across annotators, and physical plausibility of intent inferences. A skilled RLHF annotator who can reliably distinguish a helpful chatbot response from a harmful one may have no ability to identify whether a power grasp or precision pinch is appropriate for a given object. The expertise does not transfer.
What formats does Claru deliver that Surge AI does not support?
Surge AI delivers annotation outputs in standard text and JSON formats suitable for NLP and LLM training pipelines. Claru delivers data in the formats robotics and physical AI teams use: WebDataset for streaming video training at scale, HDF5 for dense numeric arrays and manipulation trajectories, RLDS/TFDS for reinforcement learning pipelines, and Parquet for tabular metadata queries. Every Claru delivery includes enrichment layers (depth maps, segmentation masks, pose estimates, optical flow) as aligned side-channels — not just annotation labels. These enrichment layers are training inputs for the model, not just metadata. Surge AI's output is labels; Claru's output is a complete training dataset.
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