Hive Moderation
Hive’s complete solution to protect your platform.
Mobilizing the world's largest distributed workforce of humans labeling data, we are raising the bar for automated content moderation. We offer both best-in-class
models as well as manual moderation, allowing us to provide solutions at scale and
outperform contract workforces of business process outsourcers (BPOs).
In addition to our best-in-class models, our distributed workforce can meet a variety of manual moderation needs. Whether you want to manually moderate user content or annotate training data at scale, our distributed system and consensus policy provide a level of precision that our competitors cannot.
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Amazon Rekognition
Amazon Rekognition makes it easy to add image and video analysis to your applications using proven, highly scalable, deep learning technology that requires no machine learning expertise to use. With Amazon Rekognition, you can identify objects, people, text, scenes, and activities in images and videos, as well as detect any inappropriate content. Amazon Rekognition also provides highly accurate facial analysis and facial search capabilities that you can use to detect, analyze, and compare faces for a wide variety of user verification, people counting, and public safety use cases.
With Amazon Rekognition Custom Labels, you can identify the objects and scenes in images that are specific to your business needs. For example, you can build a model to classify specific machine parts on your assembly line or to detect unhealthy plants. Amazon Rekognition Custom Labels takes care of the heavy lifting of model development for you, so no machine learning experience is required.
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Nima
Nima is the all-in-one platform for managing the full Trust & Safety lifecycle, from detection and policy enforcement to global compliance. It speeds up content and account reviews with leading detection providers and AI agents in one place. When human judgement is required, moderators have the full account context, policy guidance, one-click enforcement actions and quality controls they need to make consistent decisions. Every decision is logged, transparency reporting is automated, and real-time analytics surface emerging risks, spikes and trends. Nima helps platforms better protect users while running efficient, consistent, and compliant T&S operations at scale.
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Shieldstral
Shieldstral is a 3B open-weights, policy-adaptive multimodal safety classifier designed to evaluate text, images, and text-plus-image content using policies defined at inference time. Instead of relying on a fixed taxonomy of harm categories, it frames moderation as a binary question-answering task: users provide an instruction describing the evaluation context and strictness, a yes-or-no safety question, and the content to judge. The model reads the “yes” and “no” logits and converts them into a continuous, calibrated safety score, allowing applications to threshold or rank results by confidence rather than depend on a single discrete label. This formulation unifies prompt classification, response moderation, refusal detection, toxicity detection, and multimodal safety in one interface, while letting teams adapt policies without retraining the model. Shieldstral can evaluate prompts, responses, prompt-response pairs, images, and images with accompanying text.
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