AI Training & Annotation
Skilled AI Training Labor.
Industry-Leading Quality.
Domain-expert annotation teams for RLHF, preference comparisons, and model training — managed end-to-end by data professionals who care about quality as much as you do.
Scale Your Annotation Team →The industry average for RLHF annotation is 80–85%. Our domain-expert teams consistently hit 98% — because we recruit for expertise, not just availability.
What Is RLHF Annotation?
Making AI Learn What “Better” Means
Reinforcement Learning from Human Feedback (RLHF) is how leading AI models learn to be helpful, accurate, and safe. At its core, it requires human annotators to compare pairs of AI-generated responses and indicate which one is better — and why.
The quality of those comparisons determines the quality of the model. If the annotators don't deeply understand the domain — if they can't evaluate whether a mathematical proof is correct, whether a legal argument is sound, or whether code will actually compile — the training signal is noise.
That's why domain expertise isn't a nice-to-have. It's the difference between a model that reasons well and one that sounds reasonable but is wrong.
Domain Expertise
Specialists, Not Generalists
STEM
Mathematics, physics, biology, chemistry, engineering — annotators with graduate-level expertise.
Legal
Contract analysis, legal reasoning, case law — annotators with JDs and paralegal credentials.
Coding
Code review, algorithm correctness, debugging — annotators who are working software engineers.
General Knowledge
Factual accuracy, instruction following, safety evaluation — trained generalist annotators.
Our Process
Recruit. Train. QA. Scale.
Recruit for Domain Expertise
We source annotators with verified credentials in your target domain — graduate-level STEM, licensed legal professionals, or senior engineers — not general crowd workers.
Train on Your Guidelines
Every annotator completes a structured onboarding aligned to your specific annotation guidelines, edge cases, and model behavior goals before touching production data.
QA Every Batch
Calibration samples, overlap scoring, and statistical agreement checks are built into every workflow. Annotators who drift from benchmarks receive targeted feedback before continuing.
Scale and Report
Daily agreement metrics, batch-level quality reports, and dedicated QA oversight keep your RLHF pipeline running clean at any volume.
Built for AI Platform Companies and Foundation Model Builders
If you're training or fine-tuning a large language model and need reliable, scalable annotation labor — with quality you can actually measure — we built this for you.
Unlike gig-economy annotation platforms, our teams are trained, managed, and QA'd by data professionals. You get a dedicated quality lead, daily metrics, and annotation output you can trust to feed your RLHF pipeline.
Scale Your Annotation Team
Tell us about your annotation volume, domain, and timeline — we'll scope a team that fits.
Scale Your Annotation Team →See the case study →