Tools we work with
Native integration with your ML & data stack.
Labeling platforms, model orchestration, cloud infrastructure, and data pipelines. We integrate where your ML team works.
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.
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. Mental health support built into every contract. Quality scoring on every label batch. The result is data your ML team actually trusts.
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.
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.
Content moderation for user-generated platforms. Hate speech, harassment, CSAM detection, fraud, and platform policy enforcement. Mandatory rotation, mental health support, and clear escalation paths.
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.
Typical ranges across our data engagements. Your exact targets get set based on your gold standard and use case.
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.
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.
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.
Full throughput. Rolling QA on every batch. Operator rotation enforced. Weekly quality reviews. We retrain when your taxonomy evolves, which it will, often.
Medical imaging annotation isn't the same as ad-creative moderation isn't the same as financial fraud labeling. Pick yours.
Labeling platforms, model orchestration, cloud infrastructure, and data pipelines. We integrate where your ML team works.
"We compared their labeled batches against three other vendors. Theirs had inter-annotator agreement 17 points higher and the lowest critical-error rate. We moved 100% of our annotation work to them inside three months."
30-minute discovery call. Bring your taxonomy and a sample batch. We'll run a calibration round and show you exactly what our quality looks like before you sign anything.