Expert labelers, not gig workers.
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.
Your model is only as good as its signal.
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.
What we label.
Multi-modal labeling
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.
Bounding boxes & segmentation
Object detection, instance/semantic segmentation, and keypoints at pixel-accuracy.
Entity & intent labeling
NER, intent classification, relation extraction, and slot filling for dialogue systems.
Transcription & alignment
Verbatim, diarized, and force-aligned speech transcription in 90+ languages.
Video event labeling
Temporal action detection, object tracking, and behavior classification.
RLHF services
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.
Pairwise preference ranking
Side-by-side A/B ranking with detailed rationale for reward model training.
Free-form critique
Structured written critiques of model outputs for RLHF and RLAIF workflows.
Response rewriting
Expert annotators rewrite model outputs to demonstrate ideal behavior.
Red-team adversarial
Targeted harm probes and jailbreak attempts from trained adversarial annotators.
Prompt engineering & eval
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.
Eval suite construction
Custom benchmarks for reasoning, safety, factuality, coding, or agent behavior.
Adversarial prompting
Structured red-teaming for jailbreaks, PII leakage, and harmful content generation.
Prompt library design
Production-grade prompt sets for specific verticals or agent use cases.
Model-vs-model judging
Expert blind A/B evaluation across competitors, versions, or config variations.
Supervised fine-tuning
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.
Instruction-response pairs
Hand-authored pairs across reasoning, creative, procedural, and analytical tasks.
Chain-of-thought traces
Step-by-step reasoning traces authored and verified by subject experts.
Domain-adaptation data
Legal, medical, financial, engineering, or code fine-tuning corpora on demand.
Agent trajectories
Tool-use, planning, and multi-step agent trajectories with full annotations.
Five layers between annotator and ship.
Quality gates aren't a phase — they're baked into every task assignment, at every step.
Gold-set calibration
Every new annotator passes a scored gold set before taking real work.
Live sampling
5–20% of output is automatically pulled for senior review mid-production.
Peer review
Critical tasks get multi-pass consensus with arbitration on disagreement.
Drift monitoring
Dashboards flag annotators whose agreement rates diverge from baseline.
Final QA pass
Senior reviewer signs off before data leaves the platform.
Where teams use us.
Foundation model alignment
RLHF, constitutional AI, and SFT datasets for pretrained models going to production.
Domain specialization
Adapt general-purpose models to legal, medical, financial, or engineering domains.
Autonomous driving
Pixel-accurate segmentation, 3D bounding boxes, and trajectory labeling.
Search & relevance
Query-document grading, intent classification, and ranker tuning data.
Safety & red-teaming
Targeted harm probing, jailbreak discovery, and policy-compliance evaluation.
Agent evaluation
Tool-use correctness, task-completion grading, and multi-step trajectory review.
Common questions.
How do you measure quality?
Inter-annotator agreement against calibrated gold sets, per-annotator drift tracking, and project-specific rubric scoring. You see all three live on your dashboard.
Can you handle specialized domains?
Yes. We recruit credentialed experts — MDs for clinical NLP, attorneys for legal, CS PhDs for code review, linguists for low-resource languages.
What's the typical latency?
From brief to first batch: 72 hours. Standard production throughput scales from 5,000 to 50,000 items per day depending on task complexity.
Do you support annotation tooling we already use?
We work in your tooling (Labelbox, Scale Rapid, Label Studio, custom platforms) or our own portals. We'll match your workflow.
How do you prevent rater drift on RLHF?
Weekly gold-set re-calibration, rater rotation, drift dashboards, and blind re-labeling of historical items to catch concept drift over time.
Can you scale to 100+ annotators on one project?
Yes. Our largest single-project team was 340 annotators across 6 regions. Ramp-up time is typically 5 business days.
Explore the rest of our practice.
Let's make your AI better together.
Tell us what you're training, aligning, or evaluating. We'll map a delivery plan, staffing model, and timeline within one working week.