Data Annotation & Labeling
Garbage in, garbage out. The success of any custom AI model depends entirely on the quality of its training data. We provide expert, human-in-the-loop data annotation services—from drawing precise bounding boxes for computer vision to RLHF (Reinforcement Learning from Human Feedback) for LLMs.
Core Features
Computer Vision Annotation
Precise bounding boxes, polygons, and semantic segmentation for autonomous driving, retail, and manufacturing imagery.
NLP & Text Labeling
Entity tagging (NER), sentiment classification, and intent labeling for complex, domain-specific text (medical/legal).
RLHF for LLMs
Expert human reviewers ranking AI outputs to teach custom large language models nuance, safety, and brand alignment.
Strict Quality Control
Multi-tier review processes and consensus scoring to ensure 99%+ accuracy across massive datasets.
Our Process
Guideline Creation
Week 1Working with your data scientists to create an exhaustive labeling rulebook (e.g., 'Do we box the whole car, or just the visible parts?').
Tooling Setup
Week 2Configuring enterprise annotation platforms (Scale, Labelbox, or Roboflow) to ingest your raw data securely.
Pilot Batch & Calibration
Week 3Our annotators label a small pilot batch. We review this together to calibrate our understanding of your specific rules.
Scaled Production
Week 4-6Ramping up a dedicated team of trained annotators to process tens of thousands of data points rapidly.
QA & Delivery
OngoingRunning automated consensus checks and manual QA reviews before delivering the final, perfectly formatted JSON/XML dataset.
Technologies We Use
FAQ
Why not just use cheap crowdsourcing?
What is RLHF?
Is our data secure during the labeling process?
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