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What is EyeRecognize?
EyeRecognize provides a comprehensive set of APIs designed for image and video recognition, ensuring seamless integration into your applications regardless of your experience level with machine learning. Our offerings allow for the recognition of objects, people, text, scenes, and various activities within visual media, as well as the ability to detect faces and categorize NSFW content. Through our Face Detection and Analysis features, you can pinpoint all faces in images and videos while capturing detailed attributes such as gender, age, eye features, and emotional expressions. Moreover, our Text Detection functionality facilitates the extraction of text from a wide range of sources, including license plates, street signs, advertisements, and brand logos. We also excel in identifying NSFW and other potentially inappropriate content across both images and videos. With a wealth of over forty years of combined experience in crafting AI-driven applications, the EyeRecognize team has been at the forefront of employing machine learning for content moderation on social media platforms, establishing an industry benchmark. This commitment to ongoing innovation guarantees that our technology consistently leads the way in image and video analysis, adapting to the ever-evolving landscape of visual recognition needs. In an era where visual content is more prevalent than ever, EyeRecognize stands ready to empower your applications with advanced capabilities.
What is Affect Lab?
A consumer insights platform centered on technology, designed specifically for Insights teams, facilitates the mapping of insights across a range of media, digital platforms, and shopper engagements, which in turn helps in crafting emotionally impactful customer experiences while refining the customer journey to increase conversions and collect data related to emotions, attention, engagement, and visibility. Additionally, it acts as a resource for usability testing and analytics for UX teams, allowing them to measure user focus, interaction, and emotional responses as users navigate their experiences, while also enabling the evaluation of prototypes, mockups, websites, applications, and chatbots to identify vital UI elements that capture consumer interest, ultimately resulting in user experiences that are emotionally refined and boost conversion rates. Moreover, the platform harnesses Emotion Insights to develop enhanced customer experiences, employing Facial Coding APIs to evaluate emotional reactions at scale, including single and multi-face emotion recognition in everyday environments, along with recorded video emotion assessments. It also supports the testing of various stimuli across multiple formats and channels, such as videos, print ads, planograms, packaging designs, websites, mobile apps, and chatbots, ensuring an exhaustive analysis of emotional feedback. By employing this comprehensive method, brands can effectively establish a profound emotional connection with their audience, which is essential for nurturing loyalty and sustaining long-term engagement. This innovative approach not only captures vital consumer behavior insights but also drives strategic improvements in marketing and product development.
Integrations Supported
Additional information not provided
Integrations Supported
Additional information not provided
API Availability
Has API
API Availability
Has API
Pricing Information
Pricing not provided
Pricing Information
Pricing not provided
Supported Platforms
SaaS
Supported Platforms
SaaS
Customer Service / Support
Web-Based Support
Customer Service / Support
Standard Support
Web-Based Support
Training Options
Documentation Hub
Training Options
Documentation Hub
Online Training
Company Facts
Organization Name
EyeRecognize
Date Founded
2019
Company Website
eyerecognize.ai/
Company Facts
Organization Name
Affect Lab
Date Founded
2016
Company Location
India
Company Website
affectlab.io
Categories and Features
Categories and Features
Emotion Recognition
Not specified
Planogram
Not specified
Usability Testing
Not specified
User Research
Not specified
