From image labeling to 3D point clouds — we deliver high-quality, human-verified training data at scale, with the accuracy your models deserve.
The annotation industry is full of vendors who promise quality and deliver inconsistency. Here's what we do differently — and why AI teams from autonomous vehicles to LLM fine-tuning teams choose us after trying the alternatives.
Every Annotalabs project runs through a three-layer quality framework — annotator-level review, team lead spot-checks, and independent QA validation — delivering 99%+ accuracy across image, video, audio, and text datasets.
Explore More →Annotalabs provides end-to-end annotation services, from bounding boxes to RLHF preference pairs. We cover the full stack of annotation techniques for production AI development.
Entity recognition, sentiment, intent classification, and semantic annotation for NLP models and LLM training datasets.
Bounding boxes, polygons, semantic segmentation, and keypoint labeling for computer vision in autonomous systems and security.
Speech transcription, speaker ID, sentiment, event tagging. Multilingual annotation across 28+ languages and dialects.
Frame-by-frame labeling, multi-object tracking, event detection for autonomous vehicles, surveillance, and visual analytics.
Annotation isn't generic. Each industry has its own taxonomy, edge cases, and quality bar. Our annotators are trained on the domain — not just the tool.
Object detection, behavior recognition, and threat classification.
LiDAR cuboid annotation, multi-sensor fusion, pedestrian and lane detection.
Product categorization, attribute extraction, visual search datasets.
Manipulation datasets, scene understanding, pose estimation.
HIPAA-compliant annotation of medical imaging, clinical NLP, drug discovery.
Document annotation, transaction classification, sentiment labeling.
Contract clause extraction, legal entity recognition, e-discovery datasets.
Annotalabs's streamlined annotation workflows combine advanced tools, secure infrastructure, and skilled human annotators to deliver high-quality AI training datasets.
We begin by thoroughly understanding your goals and data needs. Then, we collaboratively define clear annotation guidelines to ensure consistency and accuracy in every outcome.
Your data is processed by our domain-trained teams. Every batch passes through multi-layer QA to ensure it meets your accuracy threshold before delivery.
Receive your production-ready datasets in any format you need. As your model evolves, our workforce scales with you.
Real results from data science and engineering teams that rely on Annotalabs for accurate, scalable annotation across text, image, audio, and video.
We processed over 2 million product images for our computer vision pipeline. Annotalabs's polygon annotation team delivered 99.2% accuracy on the first QA pass, faster than any vendor we'd worked with before.
We needed multilingual audio annotation for eight languages under a tight deadline. Annotalabs's team had a working pilot in 48 hours and scaled to full production within two weeks. The QA process was rigorous, and the delivery was clean.
Our autonomous vehicle team had tried three annotation vendors before Annotalabs. The difference was the human-in-the-loop review layer — edge cases that other vendors flagged as acceptable failures were caught and corrected before they reached our model.
Annotalabs is a data annotation company specializing in production-grade training data for artificial intelligence and machine learning models. We transform raw data into structured, labeled datasets across text, image, audio, video, and generative AI modalities.
We provide image annotation, video tracking, text classification, audio transcription, 3D point cloud labeling, and RLHF preference ranking.
Because we combine a dedicated workforce with a strict multi-layer QA framework. We don't use crowdsourcing; we use domain-trained specialists who learn your specific guidelines.
Every dataset passes through three layers: annotator self-review, peer review, and senior QA spot-checks. We maintain a 99.2% accuracy standard.
