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Ratings and Reviews 0 Ratings
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What is Invisible AI?
Sufficient storage is essential for keeping video recordings locally for over a month, with a strong emphasis on safeguarding data. The training of AI models requires only a limited number of samples, allowing for swift deployment within minutes. Additionally, the system offers real-time analytics that are crucial for various stakeholders involved. Cybersecurity protocols are enforced at the edge, ensuring that protection is maintained within the factory's firewall. Worker privacy is a priority, featuring options such as face blurring when necessary. Importantly, the system does not utilize facial recognition technology at all. The solution is designed to scale across multiple factories without demanding extensive infrastructure changes. It is straightforward to manage and can be integrated seamlessly with various existing enterprise software platforms. Our goal is to provide human-centered solutions that improve working conditions while upholding worker privacy. We maintain a strict policy against the use of facial recognition, concentrating exclusively on aggregated metrics. With just one click, operators' faces can be blurred, ensuring that the performance of AI remains uncompromised, which ultimately enhances both security and privacy. This comprehensive strategy not only adheres to compliance standards but also promotes a safer workplace environment, reflecting our dedication to innovation and responsibility in technology. By prioritizing these values, we aim to create a more accountable and transparent system for all users.
What is Amazon Rekognition?
Amazon Rekognition streamlines the process of incorporating image and video analysis into applications by leveraging robust, scalable deep learning technologies, which require no prior machine learning expertise from users. This advanced tool is capable of detecting a wide array of elements, including objects, people, text, scenes, and activities in both images and videos, as well as identifying inappropriate content. Additionally, it provides accurate facial analysis and search capabilities, making it suitable for various applications such as user authentication, crowd surveillance, and enhancing public safety measures.
Furthermore, the Amazon Rekognition Custom Labels feature empowers businesses to identify specific objects and scenes in images that align with their unique operational needs. For example, a company could design a model to recognize distinct machine parts on an assembly line or monitor plant health effectively. One of the standout features of Amazon Rekognition Custom Labels is its ability to manage the intricacies of model development, allowing users with no machine learning background to successfully implement this technology. This accessibility broadens the potential for diverse industries to leverage the advantages of image analysis while avoiding the steep learning curve typically linked to machine learning processes. As a result, organizations can innovate and optimize their operations with greater ease and efficiency.
Integrations Supported
Amazon Web Services (AWS)
AWS AI Services
AWS App Mesh
Amazon Augmented AI (A2I)
BotCore
Descope
Google Cloud Platform
Microsoft Azure
Microsoft Entra ID
Orange Logic OrangeDAM
Integrations Supported
Amazon Web Services (AWS)
AWS AI Services
AWS App Mesh
Amazon Augmented AI (A2I)
BotCore
Descope
Google Cloud Platform
Microsoft Azure
Microsoft Entra ID
Orange Logic OrangeDAM
API Availability
Has API
API Availability
Has API
Pricing Information
Pricing not provided.
Free Trial Offered?
Free Version
Pricing Information
Pricing not provided.
Free Trial Offered?
Free Version
Supported Platforms
SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux
Supported Platforms
SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux
Customer Service / Support
Standard Support
24 Hour Support
Web-Based Support
Customer Service / Support
Standard Support
24 Hour Support
Web-Based Support
Training Options
Documentation Hub
Webinars
Online Training
On-Site Training
Training Options
Documentation Hub
Webinars
Online Training
On-Site Training
Company Facts
Organization Name
Invisible AI
Date Founded
2018
Company Location
United States
Company Website
www.invisible.ai/product
Company Facts
Organization Name
Amazon
Date Founded
1994
Company Location
United States
Company Website
aws.amazon.com/rekognition/
Categories and Features
Computer Vision
Blob Detection & Analysis
Building Tools
Image Processing
Multiple Image Type Support
Reporting / Analytics Integration
Smart Camera Integration
Manufacturing
Accounting Integration
ERP
MES
MRP
Maintenance Management
Purchase Order Management
Quality Management
Quotes/Estimates
Reporting/Analytics
Safety Management
Shipping Management
Categories and Features
Computer Vision
Blob Detection & Analysis
Building Tools
Image Processing
Multiple Image Type Support
Reporting / Analytics Integration
Smart Camera Integration
Content Moderation
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
Deep Learning
Convolutional Neural Networks
Document Classification
Image Segmentation
ML Algorithm Library
Model Training
Neural Network Modeling
Self-Learning
Visualization
Emotion Recognition
Facial Emotions
Facial Expression Analysis
Machine Learning
Photo Emotions
Speech Emotions
Video Emotions
Written Text Emotions
OCR
Batch Processing
Convert to PDF
ID Scanning
Image Pre-processing
Indexing
Metadata Extraction
Multi-Language
Multiple Output Formats
Text Editor
Zone Selection Tool
People Counting
API
Anonymous Counting
Benchmarking
Car Counting
Conversion Tracking
Data Export
Events Statistics
Heatmaps
Mood/Age/Gender Recognition
Motion Detection
Reporting / Analytics
Retail Counting
Staff Exclusion
WiFi Tracking
Zone / Area Monitoring
Session Replay
Eye Tracking
Form Analytics
Heatmaps
Mouse Tracking
Optimization Tools
Session Recording
Surveys
User Experience Analysis
User Feedback
Visitor Segmentation