Preamble Description
Preamble democratizes a safety and security layer for generative AI systems. Our comprehensive platform and AI policy marketplace allow organizations, domain experts, and all stakeholders to curate shared values and deploy generative AI guardrails that integrate ethics, maintain security, comply with policies, and mitigate risk. Beyond applying values to AI, Preamble provides AI red-team tools to continuously improve safety guardrails.
Preamble Alternatives
ModerateContent
Trusted all over the internet to protect communities from offensive content It's easy to integrate an API into your website, app, or platform. Analyze an animated anime image and give it a rating for its content for different audiences. Detects labels and tags images. Detects any anime characters visible and names them. Detects copyrights of image content. Analyze text and return any negative words founds in 27 languages. Also, returns a cleaned version of text. Analyze an image, and return the QRcode text. Analyze an image, and rate its content for appropriateness for a specific audience (adult, teen, or everyone).
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SafetyKit
Automated Trust and Safety Decisions will help you reduce your backlog and improve precision. No eng, ML or vendor management is required. Define what policies you want and don't wish to be implemented on your platform. Our AI agents will execute these policies with greater precision and speed than humans. With our quality monitoring tools, you can deploy repeatable policies that are correct with confidence. Legacy ML models need expensive labeling and extensive training. Human reviewers require even more time and effort. We allow you deploy new enforcements without retraining agents or models. It's difficult to understand how ML classifiers predict, and human reviewers don't do much better. SafetyKit provides a detailed explanation for every decision.
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Amazon Rekognition
Amazon Rekognition allows you to easily add image and video analysis into your applications using proven, highly-scalable, deep learning technology that does not require any machine learning expertise. Amazon Rekognition allows you to identify objects, people and text in images and videos. It also detects inappropriate content. Amazon Rekognition can also be used to perform facial analysis and facial searches. This is useful for many purposes, including user verification, people counting, public safety, and other uses.
Amazon Rekognition Custom Labels allow you to identify objects and scenes in images that meet your business requirements. You can create a model to help you classify machine parts or detect plants that are sick. Amazon Rekognition Custom Labels does all the heavy lifting for you.
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Hive Moderation
Hive offers a complete solution to protect your platform.
We are the largest global distributed workforce of people labeling data and raising the bar for content moderation. We offer both best in class
We can provide scaled solutions using both manual moderation and models.
Outperform contract workforces from business process outsourcing (BPOs)
Our distributed workforce is able to meet manual moderation requirements in addition to our best-in class models. Our distributed system and consensus policy offer a level of precision that is unmatched by our competitors, whether you need to manually moderate user content or annotation training data at large scale.
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Pricing
Pricing Starts At:
$100/month/user
Pricing Information:
Custom prices available for enterprises.
Free Trial:
Yes
Company Details
Company:
Preamble
Year Founded:
2021
Headquarters:
United States
Website:
www.preamble.com
Media
Preamble safety and security layer for AI
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Product Details
Platforms
SaaS
On-Premises
Type of Training
Documentation
Live Online
Customer Support
Phone Support
Online
Preamble Features and Options
Content Moderation Software
Artificial Intelligence
Audio Moderation
Brand Moderation
Comment Moderation
Customizable Filters
Image Moderation
Moderation by Humans
Reporting / Analytics
Social Media Moderation
User-Generated Content (UGC) Moderation
Video Moderation
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