Solutions

Data Labelling, Data Annotation, and Generative AI
Services

AI & ML Data Annotation at Scale

Delivering end-to-end AI data solutions powered by a global, flexible workforce and human-in-the-loop systems. We provide high-quality, cost-efficient annotation pipelines designed for LLMs, computer vision, NLP, geospatial intelligence, and multimodal architectures. Whether you're training foundational models or refining edge-case accuracy, we’ve got you covered.

Specialized Work Types

We specialize in aligning annotation complexity with specialized talent, not just volume. Our network of expert annotators is equipped to handle complex, high-skill work including:

  • Named Entity Recognition (NER), sentiment analysis, and prompt refinement for LLMs
  • Image segmentation, object detection, and bounding boxes for vision models
  • Audio transcription, diarization, and language classification
  • Video scene classification, event detection, and temporal annotation
  • Geospatial tagging, LiDAR mapping, and multimodal fusion tasks

Tool Integrations & Enterprise Customization

We integrate seamlessly with industry-leading tools such as V7, Labelbox, and support custom workflows for in-house systems. Our platform is API-ready for rapid onboarding, QA feedback loops, golden data calibration, and model-in-the-loop training iterations.

Global Talent & Smart Matching

Our agents come from diverse technical and linguistic backgrounds, enabling multilingual, 24/7 operations and highly specialized recruiting pipelines. We source, test, and match annotators to each project’s domain requirements—be it medical imaging, legal text, financial documents, or edge-case moderation. Our AI-assisted training modules provide each project access to a gig talent pool vetted on precision, recall, and consistency; no fixed head-count ceiling.

Transparency, Feedback & Continuous Improvement

Unlike black-box outsourcing, we provide transparent metrics for every label and annotator. Our star rating system tracks accuracy, consistency, consensus, and speed—fueling a feedback loop for both individual and model performance. Clients can monitor progress in real time and make iterative adjustments.

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