HumanRef
High-fidelity human feedback and RLHF infrastructure for enterprise-grade model alignment.
Enterprise-grade automated data labeling and dataset curation for production-ready AI models.
HiHat AI is a high-performance data labeling and management platform designed for the 2026 data-centric AI landscape. It specializes in bridging the gap between raw unstructured data and model-ready ground truth through its proprietary 'Auto-Refinement' engine. Unlike traditional manual annotation services, HiHat leverages foundation models (LLMs and VLMs) to pre-annotate complex datasets, including high-resolution video and 3D LiDAR point clouds, significantly reducing the labeling bottleneck. The architecture is built for high-throughput enterprise pipelines, offering seamless synchronization with AWS S3, GCP, and Azure storage. Its core innovation lies in its 'Active Learning' loop, which intelligently identifies low-confidence samples and prioritizes them for human verification, ensuring 99.9% accuracy for safety-critical applications like autonomous driving and medical imaging. By 2026, HiHat has positioned itself as the go-to infrastructure for teams requiring rapid iteration of high-quality training data, featuring built-in dataset versioning and rigorous consensus scoring to eliminate human bias.
Uses uncertainty sampling to identify which data points will provide the most information gain for the model.
High-fidelity human feedback and RLHF infrastructure for enterprise-grade model alignment.
The industry-standard open-source platform for professional data labeling and computer vision management.
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Integrates Visual Language Models to automatically label objects based on natural language descriptions.
Multi-annotator agreement logic using Bayesian estimation to determine the true label.
Tracks object bounding boxes across video frames using optical flow and Kalman filters.
Implements a Git-like structure for data manifests, allowing rollbacks to specific dataset states.
Runs heuristic checks (e.g., box size constraints, label consistency) in real-time.
Simultaneous visualization and labeling of 2D camera feeds and 3D LiDAR point clouds.
Manually labeling millions of frames of LiDAR and video is prohibitively expensive.
Registry Updated:2/7/2026
Requires highly specialized doctors to annotate tumors with pixel-perfect precision.
Categorizing millions of user-uploaded product images into thousands of classes.