Annotation, moderation, and AI quality, done right.
Data labeling, content moderation, RLHF, and AI evaluation at scale. Operators trained, rotated, and supported. Native experience with AI-first companies where label quality is the difference between a good model and a great one.
Bad labels make bad models.
You can have the most sophisticated model architecture in the world. If the data feeding it was labeled by burned-out annotators clicking through batches at speed, the model will be brittle in production. Every AI-native team eventually learns this the hard way.
Same with content moderation. A platform with thousands of users posting per hour can't lean on community reporting alone. You need humans reviewing the hard edge cases that automated systems get wrong. And those humans need to be supported, rotated, and protected, or the work breaks them.
We do this work the way it should be done. Domain-trained annotators. Mandatory rotation policies for moderators on graphic content. Operator welfare safeguards built into every contract. Quality scoring on every label batch. The result is data your ML team actually trusts.
Four workstreams.
One data quality engine.
Data labeling at scale
Text, image, audio, video, and multimodal annotation. Bounding boxes, NER, entity linking, intent classification, sentiment, semantic segmentation. Tuned to your taxonomy and validated against your gold standard.
RLHF & AI evaluation
Reinforcement learning from human feedback. Model output ranking, eval rubrics, red-teaming, and adversarial testing. Operators trained on prompting and model behavior, not just labeling.
Trust & safety moderation
Content moderation for user-generated platforms. Hate speech, harassment, CSAM detection, fraud, and platform policy enforcement. Mandatory rotation, operator welfare safeguards, and clear escalation paths.
Quality assurance & gold sets
Multi-pass review on critical batches. Inter-annotator agreement tracking. Gold-set calibration before every project. We don't ship data your ML team will quietly distrust.
Real numbers.
The kind your ML team trusts.
Ranges we target across our data engagements. Your exact targets get set based on your gold standard and use case.
Labeling from day one.
Quality compounds from there.
Calibrate
We review your taxonomy, edge cases, and gold standard. We run a calibration batch with your team to align on judgment calls. Disagreements get documented in the rubric, not glossed over.
Train the team
Annotators hired or assigned based on domain match. Multi-day training on your rubric. Practice batches scored against gold. Operators don't touch live data until they pass the gate.
Pilot batch
First live batch with intensive QA. Inter-annotator agreement reported daily. Rubric refinements roll out the next morning. Your ML lead sees every quality metric in real time.
Scale & sustain
Full throughput. Rolling QA on every batch. Operator rotation enforced. Weekly quality reviews. We retrain when your taxonomy evolves, which it will, often.
Different models, different labels.
Data work tuned to each.
Medical imaging annotation isn't the same as ad-creative moderation isn't the same as financial fraud labeling. We build for yours.
Native integration.
With the stack you already run.
Labeling platforms, model orchestration, cloud infrastructure, and data pipelines. We integrate where your ML team works.
Questions buyers ask us.
How do you protect your moderation team?
What labeling platforms do you support?
Can you handle multimodal and reasoning-task labeling?
What's your IP and data-handling posture?
How do you scale up or down with our needs?
How is this priced?
Ready to keep your platform safe and your data clean?
AI-first · Human-Driven.
One call and we'll show you.