High-quality annotated datasets are the foundation of intelligent AI systems. Leafcode delivers accurate, scalable data annotation tailored to your ML models.
End-to-end expertise for every business challenge
Image Annotation
Design Lead
2023–Present
Image annotation involves labeling visual data through techniques such as object detection using bounding boxes or polygons, image classification (single or multi-label), and both semantic and instance segmentation for detailed scene understanding. It is widely applied in computer vision systems, autonomous driving technologies, and retail analytics.
Video Annotation
Role
2021-2022
Video annotation focuses on labeling moving visuals frame by frame while maintaining temporal accuracy, including object tracking, action detection, and multi-object interactions across sequences. It plays a critical role in surveillance systems, activity recognition, and automated content moderation.
Text Annotation
Nova Technologies
2021-2022
Text annotation enhances language datasets through processes like named entity recognition (NER), sentiment detection, intent categorization, and topic tagging. These techniques support NLP applications such as chatbots, language models, and intelligent content analysis systems.
Audio Annotation
Role
2021-2022
Audio annotation includes transcribing speech, identifying phonetic elements, detecting environmental sounds, distinguishing speakers, and recognizing emotions or tone variations. It is essential for voice assistants, speech processing engines, and advanced audio analytics platforms.
Step 01
Discover
We learn your annotation goals, data landscape and ML objectives
Together we define the labeling schema, quality standards and success metrics
We scope the volume, timeline and resources needed
A clear roadmap is established before any work begins
Step 02
Setup
Raw data is ingested, cleaned and organized
Formats are standardized and quality assessed
Workflows and quality baselines are configured
Reference sample annotations are created for annotator alignment
Step 03
Pilot
A proof-of-concept batch is executed with trained annotators
Real-time quality monitoring and iterative feedback loops run in parallel
Accuracy is verified through multi-level validation and consensus checks
Edge cases are documented and the approach is confirmed before scaling
Step 04
Steady-State
Full-production delivery kicks off with continuous QA and optimization
Datasets are delivered organized, formatted and fully documented
Metadata, annotation statistics and audit trails are provided
Seamless handoff and integration into your ML pipelines
End-to-end expertise for every business challenge