Labelbees empowers AI to see, understand, and act with precision. We help enterprises build high-performance visual systems with expert input data, precise object tracking, scene understanding, and advanced evaluation tools, enabling production-ready AI across industries.
Labelbees designs tailored workflows for your specific industry and model requirements. Our expert humans and AI-assisted tools deliver scalable, accurate, and consistent results every time.
Vision-Language Reasoning
Object Detection
Object Tracking
Semantic Segmentation
Instance Segmentation
Image Classification
Pose Estimation
Landmark Detection
Document Extraction
Leverage high data operation standards to ensure fast turnaround times, even for the most complex vision datasets. Combined with rigorous QA protocols and metadata enrichment, we help you deploy AI faster with confidence.
Scalable workforce trained in vision-specific tasks
AI-assisted annotation for speed and consistency
Custom QA workflows with edge-case flagging
Metadata tagging to improve model context and performance
Whether you’re training models in the cloud or deploying with custom computer vision pipelines, we adapt to your workflow.
A leading geospatial analytics company needed to build a high-performance AI model to detect and classify vessels from satellite imagery. Their challenge: managing complex maritime environments with densely packed vessels, occlusions, variable orientations, and visually similar non-vessel objects. Existing labeling solutions were too slow, costly, or lacked the precision required for mission-critical applications. Labelbees AI delivered expert-in-the-loop annotations at scale, powering accurate, high-speed vessel detection with our human-led, AI-assisted pipeline. We built a comprehensive vessel ontology covering 100,000+ examples, developed specialized workflows to handle edge cases, and applied rigorous QA to ensure consistency across maritime scenes.
Reduced annotation cycle from 14 days to just hours
Achieved first-pass approval with no costly rework
Boosted annotation efficiency by 15%