An intuitive Image Segmentation Solution that helps partition an image input into multiple segments for simplified image analysis. Backed by advanced AI services of AWS, the tool partitions a digital image into fragments based on various factors like pixel intensity value, colour, and texture. The solution leverages AWS Semantic Segmentation to collectively assign label(s) to every subdivided segment in an image surrounded by a bounding box.
In addition, it utilizes AWS Rekognition for detecting labels and categorising existing segmentation algorithm into region-based segmentation, data clustering, and edge-base segmentation.
Our Image Segmentation solution can be used across a broad spectrum of industries, including Robotics, Traffic Control System, Sports Analytics, Video Surveillance, Medical Imaging and Diagnostics, Autonomous Vehicles, Livestock Management, and Media & Marketing.

A personalized and responsive image classification model to identify images and objects from rich media (digital image files). It offers enhanced search capabilities to identify hundreds of classes of objects - including people, activities, animals, plants, and places and categorizes images based on metadata, colour and other factors.
R Systems’ Image Classification Model is underpinned by deep neural nets that can learn higher-level representations (features) of input images. It utilizes Amazon Rekognition to identify objects, people, texts, scenes, and activities in images, as well as to detect any inappropriate or copied content.
Our Image Classification Model can be customized to fit into your unique business requirements. You can feed the platform with your unique datasets and start training it to work the way you want. R Systems leverages Amazon SageMaker to deploy this customized model through Amazon SageMaker image classification algorithm.

An all-inclusive face recognition solution that provides a fast, accurate, and non-invasive means for identifying human faces. It maps distinguishable facial features, patterns in the visual data, and compares new images and videos in the extensive libraries. The solution leverages neural networks and deep machine learning to make the technology a usable reality.
It utilizes Amazon Rekognition to recognize faces in images and videos; BOTO 3 (SDK for Python) to access object-oriented APIs; and Python & OpenCV/ CV2 to activate and capture real-time videos from the camera.
The tool effortlessly recognizes multiple faces simultaneously, including emotions, age, and gender at an unparalleled speed and scale. Buoyed by ironclad security system, R Systems' high-end facial recognition application allows users to add new tags within the current database for any existing or add-on face right away.

Equipped with advanced and highly-responsive AI, our voice-activated bots and text-based Chatbots can effectively handle diverse patient requests across a broad spectrum of activities both in the clinical and the administrative areas.
R Systems' Chatbot/Speechbot utilizes Amazon Lex and Amazon Transcribe to provide deep functionality and improved flexibility of natural language understanding (NLU) and automatic speech recognition (ASR). It also utilizes AWS Polly – a text-to-speech (TTS) service - to synthesize speech. This enables organizations to build bespoke bots for highly engaging user experience.
In addition, our platform leverages AWS Lambda to offer serverless computing platform & Elastic Compute Cloud (EC2) to enable scalable compute capacity.

Equipped with advanced and highly-responsive AI, our voice-activated bots and text-based Chatbots can effectively handle diverse patient requests across a broad spectrum of activities both in the clinical and the administrative areas.
R Systems' Chatbot/Speechbot utilizes Amazon Lex and Amazon Transcribe to provide deep functionality and improved flexibility of natural language understanding (NLU) and automatic speech recognition (ASR). It also utilizes AWS Polly – a text-to-speech (TTS) service - to synthesize speech. This enables organizations to build bespoke bots for highly engaging user experience.
In addition, our platform leverages AWS Lambda to offer serverless computing platform & Elastic Compute Cloud (EC2) to enable scalable compute capacity.

A Sagemaker R Kernel using customized collaborative filtering algorithm that delivers intelligently-generated product recommendations by extracting hidden insights around historical user-product interactions. It digs into similar type of products leveraging historical user ratings through correlation pointers.
Driven by machine learning, our interest-based recommendation solution leverages AWS Sagemaker to provide personalized recommendations for customers, and item based collaborative filtering to provide recommendations during a session. Moreover solution sends email notification alert to the user around product recommendation if it finds strong affinity and relationship with the user leading to higher sales for the business.
Businesses can predict what they want next; dynamically present tailored recommendations; and drive innovation on all fronts - integrating AWS SageMaker trained model into their existing process.

A distinctive document classification model that quickly analyzes and tags documents (such as newspaper articles, web pages, advertisement copies, blogs, articles, emails, and social media updates) for easier discovery.
R Systems leverages Amazon SageMaker to deploy its Document Classification model on embedded systems and edge-devices as well as Amazon SageMaker image classification algorithm to support multi-label classification.
Our distinctive Document Classification tool is applicable across an extensive range of processes including customer support, legal support, branding & advertising, scientific publication, and human resource.