Benchmark item
Mathematics
Prove or disprove: every sufficiently large even integer is the sum of a prime and a semiprime under the stated constraint.
Item bank · written past every training cutoff
NewCUA Dataset – long-horizon tasks for agent training
Expert Data for
Expert Data for
Frontier Research
Frontier models have read everything ever written. We turn what experts know beyond that corpus into what the next model learns.
Why SuperAnnotate
Why frontier labs work with us
Expert depth, one platform for every vendor, and operations that own delivery – the three pieces every research data engagement needs.
Experts with real depth
Practicing specialists – physicians, proof-writers, litigators – whose knowledge lives outside any training set. They author the benchmark items that locate where your model breaks, and carry the authority to grade the answers.
One platform, every vendor
Bring every data vendor you work with onto the #1-rated platform. Consensus scoring, inter-annotator agreement, gold sets, item-level audit trails – the same QA depth on every pipeline, whoever runs it.
Fully managed operations
Dedicated PMs and AI Ops who own quality and speed on every delivery – scoping, staffing, and item-level tracking, so your researchers stay on research.
What we run for labs
Across the full post-training loop
Expert signal across training, agent environments, evaluation, and benchmark design.
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Train RLHF & SFT
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Agents RL Environments & Agents
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Evaluate Evals & Red Teaming
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Benchmark Benchmark Design
The Platform
Infrastructure built for research pipelines
Every program above runs on the same platform – interfaces, review layers, and orchestration that reshape around whatever the research calls for.
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Multimodal, low-code interfaces
Interfaces fully customized to any task and combination of data types – text, image, audio, or video – in seconds, so complex datasets never wait on tooling.
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Model-in-the-loop
Human annotators augmented with AI in one workflow – model pre-labels and flags, experts judge and correct. Faster, more consistent dataset creation.
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Advanced workflows
Code execution, database queries, guardrails, and routing between multiple experts – orchestration for pipelines that plain annotation tools can’t express.
From the labs
What researchers say
SuperAnnotate's deep understanding of our research requirements, combined with their ability to recruit highly qualified human reviewers and deliver thoughtful, framework-driven solutions, has super-powered our ability to efficiently build high-quality benchmarks.
Combining our domain experts with outsourced teams inside one platform significantly improved our iteration speed. Spotting errors early saved us considerable downstream effort.
They stood up a bench of PhD-level reviewers for our agent evaluations in days, not months. Expert throughput simply stopped being our bottleneck.
Catalog
Off-the-shelf data for agents, robots, and the models after that.
A growing licensed catalog: computer-use trajectories, RL environments, robotics data. Samples available first.
Dataset
Quantity
Action
cua_trajectories_v2Long-horizon computer-use tasks across real applications
41,300 items
sample ↓
rl_env_suiteReward-annotated environments for agentic training
120 environments
sample ↓
robotics_teleop_v1Bimanual teleoperation episodes with labeled states
9,800 episodes
sample ↓
next on the catalogNew sets land regularly – ask what's in production
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soon
Push past what
today's models can do
Bring us the problems your models still fail – we'll bring the experts, the platform, and the operations.