Human-led enrichment for training, alignment, prompting, and fine-tuning. Every annotator is vetted, trained, and supervised by a regional lead who knows them by name.
Generic crowdsourcing doesn't train frontier models — thoughtful human judgment does. Our annotators aren't task-shopping through a marketplace; they're career labelers, domain experts, and specialists who've been with us for years.
Every project runs through structured QA: sampling, gold-set validation, multi-pass review, and expert arbitration on disagreements. The result is signal you can train on — not noise you have to filter.
Text, image, audio, video, and sensor data — with the same rigor at every modality.
We handle every labeling task modern AI training throws at annotators — from bounding boxes to NER to dialogue state tracking — with quality benchmarks that match or exceed top-tier academic datasets.
Object detection, instance/semantic segmentation, and keypoints at pixel-accuracy.
NER, intent classification, relation extraction, and slot filling for dialogue systems.
Verbatim, diarized, and force-aligned speech transcription in 90+ languages.
Temporal action detection, object tracking, and behavior classification.
Preference ranking, critique, and rewrite workflows for alignment-grade datasets.
Aligning a frontier model is judgment work — and judgment doesn't scale on a marketplace. Our RLHF annotators are trained on your rubric, calibrated against gold sets, and rotated to prevent rater drift.
Side-by-side A/B ranking with detailed rationale for reward model training.
Structured written critiques of model outputs for RLHF and RLAIF workflows.
Expert annotators rewrite model outputs to demonstrate ideal behavior.
Targeted harm probes and jailbreak attempts from trained adversarial annotators.
Evaluation harnesses, prompt libraries, and red-teaming built by domain experts.
We build the prompts and evals that stress-test your models — not the prompts that live in example notebooks. Domain experts write, blind-test, and grade every set.
Custom benchmarks for reasoning, safety, factuality, coding, or agent behavior.
Structured red-teaming for jailbreaks, PII leakage, and harmful content generation.
Production-grade prompt sets for specific verticals or agent use cases.
Expert blind A/B evaluation across competitors, versions, or config variations.
Instruction datasets, conversational corpora, and domain-adaptation data at scale.
Need 10,000 high-quality instruction-tuning pairs in legal domain English, Spanish, and Arabic? We produce that in two weeks — written by actual lawyers, not templated from web data.
Hand-authored pairs across reasoning, creative, procedural, and analytical tasks.
Step-by-step reasoning traces authored and verified by subject experts.
Legal, medical, financial, engineering, or code fine-tuning corpora on demand.
Tool-use, planning, and multi-step agent trajectories with full annotations.
Quality gates aren't a phase — they're baked into every task assignment, at every step.
Every new annotator passes a scored gold set before taking real work.
5–20% of output is automatically pulled for senior review mid-production.
Critical tasks get multi-pass consensus with arbitration on disagreement.
Dashboards flag annotators whose agreement rates diverge from baseline.
Senior reviewer signs off before data leaves the platform.
RLHF, constitutional AI, and SFT datasets for pretrained models going to production.
Adapt general-purpose models to legal, medical, financial, or engineering domains.
Pixel-accurate segmentation, 3D bounding boxes, and trajectory labeling.
Query-document grading, intent classification, and ranker tuning data.
Targeted harm probing, jailbreak discovery, and policy-compliance evaluation.
Tool-use correctness, task-completion grading, and multi-step trajectory review.
Inter-annotator agreement against calibrated gold sets, per-annotator drift tracking, and project-specific rubric scoring. You see all three live on your dashboard.
Yes. We recruit credentialed experts — MDs for clinical NLP, attorneys for legal, CS PhDs for code review, linguists for low-resource languages.
From brief to first batch: 72 hours. Standard production throughput scales from 5,000 to 50,000 items per day depending on task complexity.
We work in your tooling (Labelbox, Scale Rapid, Label Studio, custom platforms) or our own portals. We'll match your workflow.
Weekly gold-set re-calibration, rater rotation, drift dashboards, and blind re-labeling of historical items to catch concept drift over time.
Yes. Our largest single-project team was 340 annotators across 6 regions. Ramp-up time is typically 5 business days.
Tell us what you're training, aligning, or evaluating. We'll map a delivery plan, staffing model, and timeline within one working week.