The power of Data via
Artificial Intelligence and Machine Learning
Train algorithms to detect humans and their gestures for the context of many use cases
Train computer vision-based algorithms to detect pretty much anything. All we need is sample data to train. We can detect humans, equipment, attire, activities, gestures, lesions, etc.
Build a searchable knowledge graph and actionable insights
Involves semantic analysis of text data like documents, email, webchat, social media, surveys, customer forums, etc. along with the process of building a searchable knowledge graph and insights.
Powerful AI engine trained on reams of data to understand customer needs
Chatbots can be trained to interpret and answer a wide range of questions for enhancing data accuracy, domain-specific and near human-like cognition.
Python is modern, flexible, and has native support for a vast array of Machine Learning platforms. Also, acts as a primary programming language for building our Machine Learning pipeline.
TensorFlow is Google’s open-source deep-learning platform to build Computer Vision-based models to run in small, lower-powered edge CPUs as well as cloud-based GPUs.
OpenCV is a library of programming functions for real-time computer vision. The go-to platform for image processing needs like extract, pre-process, tags, visualize and report on video data
OpenVino is Intel’s open-source platform for running optimized computer vision models on Intel’s processors. We have built extensive expertise in building solutions using OpenVino.
OpenPose is an open-source platform for human pose estimation. We extensively use this platform in applications that use activity and posture recognition
Flask and Django, the preferred web platforms for building web APIs of machine learning models along with dashboards and visualization UI
MQTT, the preferred light-weight protocol for data communication between IoT sensors, edge CPUs, and servers works well on low-power IoT devices.