From RLHF preference data and model evaluation to coding benchmarks and safety red-teaming, AI labs and MLOps teams need contributors who understand the full model lifecycle, not just annotation instructions. We supply vetted human experts, structured pipelines, and quality controls built for production-grade ML at every stage.
Model quality degrades when training data programs are built around volume-first thinking. The real bottleneck is finding contributors who can produce signal, not noise.
From generalist annotation tasks to highly specialized coding and STEM domains, we source, vet, and manage contributors who produce training data your models can learn from.
We don't source from open crowdsourcing pools. Our contributors are vetted for relevant domain knowledge, communication quality, and adherence to structured annotation guidelines.
Training coding LLMs requires contributors who can actually write and evaluate code. Our STEM contributors include software engineers, data scientists, and technical specialists across 25+ programming languages.
Multimodal datasets for real-world AI systems. We annotate image, video, and sensor data with the precision robotics and embodied AI models require.
We work with your existing annotation platforms or design structured workflows from scratch, including onboarding, calibration tasks, quality benchmarks, and regular feedback loops.
As your model training program scales, so does the complexity of managing contributor pipelines. We provide operational support to maintain throughput without sacrificing quality.
Quality is a process, not a promise. Our approach embeds quality controls at every stage of the contributor pipeline.
Domain-specific screening tests, background review, and calibration tasks before contributors are assigned to any production work.
Structured onboarding aligned to your annotation guidelines. Calibration tasks establish baseline accuracy before full task access.
Quality checks embedded in the annotation workflow: inter-annotator agreement, consensus review, and regular spot audits.
Contributor performance tracked over time. Low performers cycled out proactively. Feedback maintains alignment with evolving standards.
Whether you're scaling a frontier model or shipping applied AI, we work with the people responsible for training data quality and contributor operations.
You're managing RLHF pipelines, evaluation programs, and annotation quality at scale. You need contributors who can handle multi-step tasks with domain nuance, and a partner who can ramp capacity without sacrificing quality.
You're shipping models to production with a lean team. You need a data partner who can move fast, source the right specialists, and integrate with your existing annotation tooling and model lifecycle workflows.
You're building the infrastructure that powers model training and evaluation. You need reliable contributor pipelines that plug into your platform, with consistent throughput, quality monitoring, and scalable capacity planning.
Tell us about your use case (domain, volume, quality requirements) and we'll scope a contributor program built around what your pipeline needs.