Frontier AI data partner
The vetted human roster behind frontier AI.
Caudal delivers high-quality human signal for model evaluation, post-training, and benchmarking. Built by a vetted network of engineers, researchers, and domain experts.
Taskers
Tasks delivered
Throughput
How do you want to work with us?
Tell us which describes your team and we'll connect you with the right people.
Benchmark-grade evaluation and training data.
Strengthen benchmarks and access research-grade eval and training data to advance model reasoning and capability.
Talk to a researcherFor AI PlatformsDomain-specific data at scale.
Get human-evaluated datasets built for your product's specific capabilities and post-training requirements.
Talk to our teamFor Enterprise AI TeamsReliable data pipelines for your AI stack.
Build vetted, human-evaluated data for the AI systems and workflows your team depends on.
Talk to an expertWhat labs get
Quality signals your team can operationalize.
We route screened experts into your programs and track every task through review, so you see status, domain coverage, and throughput — not just a finished file.
Active programs
We're building data for these benchmarks now.
Each program has its own rubric, reviewer pool, and quality bar. Tell us your benchmark and we'll match the supply.
Quality assurance
Nothing reaches you without passing review.
Every task goes through a structured quality process before it enters your dataset. Work that doesn't meet the bar is returned — not delivered. You get signal you can trust, not volume you have to re-clean.
Automated screening
Every submission is checked against defined quality criteria before it enters the pipeline. Anything that fails is flagged immediately.
Structured scoring
Tasks are evaluated across multiple quality dimensions. Each verdict — pass, flag, or reject — is backed by evidence, not a heuristic.
Human sign-off
Edge cases go to a senior reviewer before delivery. Nothing borderline ships without a person making the final call.
The talent behind the work
2,000+ vetted contributors across software engineering, mathematics, scientific reasoning, and AI research. Calibrated signal — not crowd-sourced volume.
What we work on
Code quality evaluation
Grading agentic coding submissions against unit-test rubrics, evaluating solution quality across multiple valid implementation approaches.
Benchmark authoring
Writing contamination-resistant coding problems with precise rubrics, designed to test frontier model capabilities.
Adversarial testing
Constructing adversarial test cases that expose model failures in code generation, debugging, and multi-step reasoning.
Frequently Asked Questions
Common questions, answered plainly.
- How do you ensure data quality?
- Every submission goes through a structured quality process before it reaches you. Work is screened automatically, scored against defined criteria, and — where there's any doubt — reviewed by a senior analyst. Nothing borderline ships without a human making the final call.
- How are contributors vetted?
- Every contributor completes a two-section timed assessment covering software engineering, CLI tooling, code evaluation, and scenario reasoning. General assessment is auto-scored; scenario based assessment responses are reviewed by our team. Only those who clear both assessments are admitted to the roster.
- What task types do you cover?
- Evaluation and training data across coding (SWE-bench, Terminal-Bench, SWE-bench Pro), scientific reasoning, instruction following, adversarial probing, and rubric QA. If your benchmark has a different format, talk to us — we scope custom programs.
- Can you handle a custom benchmark or internal eval?
- Yes. We run bespoke programs built around your rubric, reviewer pool, and quality bar. We handle calibration, contributor matching, and review. You receive the finished dataset.
- What does turnaround look like?
- Program setup typically takes a few days. Once contributors are matched and calibrated, throughput is continuous — not a single delivery. You see status, domain coverage, and task-level progress, not just a finished file at the end.
- How do we get started?
- Use the contact form to describe your program. We'll follow up to scope requirements, benchmark format, and timeline. No commitment required at that stage.
Work with us
Ready to build better AI evaluation data?
Tell us about your program. We'll match the right contributors and get your pipeline running.
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