We build your mobile application using the latest technologies and languages. We enable user collaboration, tracking and routing, server-side simulations, statistical analysis and more. Some of the technologies we use include: native Android, native iOS, client & server architecture, GEO Mapping, Mobility & Tracking, augmented reality.
Often, we conduct web development as part of a complete solution that includes web, mobile, and cloud components. We specialize in web front end, database, server side and cloud. Some of the technologies we use include:
We use best practices, protocols and standards in developing for IoT solutions. We have conducted projects that include sensor connectivity, signal processing, embedded environment integration, and network and cloud communication. We are experienced in using IoT infrastructure, network, data management and device protocols.
Our comprehensive service includes building an automation lab and a full testing system, running the testing and ensuring product functionality on every device. Our clear aim is to shorten release cycles and improve the final product. As a partner of Perfecto Mobile, we provide development and testing system maintenance for mobile, web, client-server, load/performance and more. Read the QA automation project at Check Point.
As part of full project deployment, we offer complete DevOps solutions such as installation, production upload, continuous integration, activation, resource monitoring and more. The technologies we deploy include Chef and Jenkins, major cloud services such as Azure, AWS and IBM, and Bash and Python scripting.
Machine and Deep Learning
Our machine learning projects start with Big Data handling including data organization, storage, and modeling; working with technologies such as Hadoop and Elasticsearch. We then deploy pattern recognition, data mining and knowledge discovery, using various algorithms: from simple linear regression through Naive Bayes, decision trees and random forest, to deep neural networks such as RNN and CNN. We normally conduct implementation using deep learning frameworks such as Keras, TensorFlow, MXNet, Theano, Torch and Caffe.