Great AI starts with great data. Fusion CX turns raw text, images, video, audio and sensor data into clean, human-labeled training data, from RLHF to physical AI training data for robots, in 28 languages with 99%+ accuracy.
Data annotation labels and tags raw data so machines can understand it, from drawing boxes around objects to tagging sentiment or transcribing speech. Outsourcing it lets your engineers focus on building models instead of labeling by hand.
Every project runs on Omind AI’s platforms: Annotera.AI for multimodal data labeling and RLHF, and Roborax.AI for physical AI and robotics training data, with human-in-the-loop quality control in one connected pipeline.
We run live customer experience operations for global brands, and we put that understanding of real human interactions to work in our labeling.
The same trained annotators work on your project, so quality and context stay consistent.
With 17.5K+ agents across global operations, Fusion CX both trains AI and deploys it.
A multi-country delivery network handles high-volume, multilingual projects without sacrificing accuracy.
Annotator review, team-lead spot checks and independent QA catch errors before delivery.
Each project runs through a layered quality process, so what you get back is ready to train on.
Named entity recognition, sentiment tagging, intent classification and semantic labeling for search, chatbots and LLMs.
Bounding boxes, polygons, semantic segmentation and keypoint labeling for computer vision, from catalogs to medical imaging.
Frame-by-frame object tracking, event tagging and multi-object labeling for autonomous systems and behavior analysis.
Transcription, speaker identification, sentiment and intent labeling across accents and languages for voice AI.
Human feedback, prompt and response ranking, red-teaming and content moderation to align LLMs with human judgment.
Watchers, resolvers and teleoperation that capture, guide and correct real-world robot behavior.
Our annotation and AI training data services power models where accuracy and privacy matter most.
Perception, navigation and safety data for machines operating in the real world.
Medical imaging and clinical text annotation, with privacy-first workflows.
Product catalogs, visual search and review sentiment labeling.
Training data for fraud detection, document processing and risk models.
Object and event detection in video.
Tell us about your data types, volumes and accuracy targets. We’ll scope a pilot or an enterprise pipeline for your models and respond within one business day.
Guides for teams building high-quality AI training data.
What AI teams ask before outsourcing data annotation to Fusion CX.
We offer data annotation customized for industries including autonomous vehicles and robotics, healthcare and life sciences, retail and ecommerce, banking and financial services, and security and surveillance.
We annotate text, images, video, audio and sensor data, including named entity recognition, bounding boxes, semantic segmentation, transcription, RLHF and physical AI training data for robotics.
RLHF (Reinforcement Learning from Human Feedback) is a training method where humans rank and rate model outputs to align AI behavior with human judgment. Yes, we provide RLHF, prompt and response ranking, and red-teaming for large language models.
Every project runs through a layered quality process: annotator self-review, team-lead spot checks and independent QA. This is how we reach 99%+ accuracy across large, multilingual projects.
Yes. We offer watchers, resolvers and teleoperation services to capture, guide and correct real-world robot behavior, feeding corrected data straight back into model training.
Both individual contributors training their own AI models and B2B or enterprise teams building production ML systems. We scale from small pilot projects to high-volume enterprise pipelines.
