Zanusys delivers cutting-edge data annotation services designed to power AI and machine learning models with precise, scalable labelled data. Our solutions align with 2026 industry trends, including AI-assisted annotation, multilingual support, and human-in-the-loop quality control to ensure superior model performance.
We specialise in multimodal annotation across text, images, audio, video, and 3D LiDAR, tailored for NLP, computer vision, speech recognition, and autonomous systems.
Bounding boxes, polygons, semantic segmentation, and keypoint annotation for object detection and medical imaging applications.
Named entity recognition, sentiment analysis, intent detection, and linguistic tagging — ideal for multilingual projects spanning 38+ global languages.
Speech-to-text transcription, speaker identification, and emotion detection for voice assistants and ASR/ITN systems.
Object tracking, pose estimation, and event labelling for surveillance, robotics, and intelligent monitoring systems.
Point cloud annotation and sensor fusion to support autonomous driving and geospatial AI solutions.
The data annotation market is driven by advancements in real-time annotation, synthetic data augmentation, RLHF/RLAIF methodologies, and bias mitigation strategies.
Zanusys integrates automation technologies such as active learning and programmatic labelling with expert linguists to achieve 98%+ accuracy, while ensuring full privacy compliance, including GDPR and ISO 27001 standards.
Our offshore delivery model from India enables cost-effective scalability for high-volume, domain-specific projects, including complex tasks such as inverse text normalisation.
As part of a broader IT staffing and AI/ML ecosystem, Zanusys complements recruitment, linguistic services, and operational support to provide complete client solutions.
We deliver through secure platforms featuring QA dashboards, customised workflows, and 24/7 global teams — with proven expertise across geospatial, healthcare, and enterprise AI sectors.
Our hybrid model enables faster model deployment, reduced bias, and measurable return on investment — combining the precision of human expertise with the efficiency of automation.