AI Data Prodigy (Prodigy by Explosion)
Scriptable machine teaching and active learning for production-grade AI training data.
The data-centric AI platform for high-quality training data and model evaluation.
Kili Technology is a premier Data-Centric AI platform designed to manage the entire lifecycle of training data, moving beyond simple annotation into complex data quality management. By 2026, Kili has solidified its position as the enterprise standard for high-stakes industries like healthcare, defense, and autonomous systems. Its technical architecture centers on a robust GraphQL API and Python SDK, enabling seamless integration into automated MLOps pipelines. The platform excels in 'Model-in-the-Loop' workflows, where pre-annotations and active learning significantly reduce labeling costs while increasing precision. Kili's 2026 iteration features advanced support for RLHF (Reinforcement Learning from Human Feedback), specifically tailored for fine-tuning Large Language Models. It provides a collaborative environment where domain experts, data scientists, and professional annotators can synchronize through rigorous QA workflows, inter-annotator agreement (IAA) metrics, and programmatic labeling. Whether deployed via SaaS, VPC, or on-premise, Kili offers the governance and security features required for SOC2 and GDPR compliance, ensuring that sensitive data assets remain protected while being optimized for high-performance model training.
Allows users to write scripts or use heuristics to label thousands of data points automatically based on defined logic.
Scriptable machine teaching and active learning for production-grade AI training data.
Enterprise-grade data labeling platform for high-performance computer vision and sensor fusion.
Verified feedback from the global deployment network.
Post queries, share implementation strategies, and help other users.
Dedicated UI for ranking LLM outputs, identifying hallucinations, and fine-tuning alignment.
Multi-stage review processes with blind labeling and consensus metrics.
Seamless integration of pre-trained models to suggest annotations that humans only need to verify.
Specialized viewer for medical imaging including windowing, 3D reconstruction, and multi-planar views.
Identifies which unlabeled data points would be most beneficial for the model to learn from next.
JavaScript-based plugins to customize the annotation interface for highly specific domain tasks.
Identifying and tracking moving objects across video frames with high temporal consistency.
Registry Updated:2/7/2026
Ensuring AI responses are helpful, harmless, and honest according to company policy.
Accurate segmentation of tumors in complex MRI scans.