DataArt is a trusted technology partner that can help building efficient, automated, highly accurate systems using modern AI technology.
Automating decision-making routine, forecasting events, probabilistic analysis, user personalization
Advanced texts, speech and cognitive analytics. Structured and unstructured data. Chatbots
Visual classification of objects nature, image recognition, real-time video processing
Advanced data analytics, clustering, pattern detection, statistical analysis, data visualization
Artificial Intelligence and Data Science project methodology is significantly different from traditional research for software delivery projects.
It requires companies to:
DataArt can help to bootstrap AI capabilities, or fill data and analytics gaps for companies that do not have the expertise internally or do not want to hire new talent until the benefits of AI are proven.
DataArt focuses not only on research, but also on delivering end-to-end solutions starting with solution design and ending with deployment of ML-model and integration into the existing or newly developed client environment.
Our main value is to deliver valuable and cost-effective solutions to our clients.
That’s why we developed an approach to R&D projects that allows us to see the progress at every stage and deliver solutions incrementally, allowing clients to decide if additional efforts are worth investment or a change of direction is required.
DataArt engineers work with the most popular modern technologies including world-leading cloud based MLaaS solutions and classic or deep learning open-source libraries.
Based on recent advances of telemedicine and the strong evidence supporting its role, DataArt has developed a prototype of a telehealth platform that utilizes AI and further demonstrates the potential of telehealth solutions.
The Telehealth AI Assistant platform improves physicians’ productivity by allowing them to focus on crucially important information rather than spending time collecting trivial data or completing encounter reports. Service representatives process patient calls and gathers required information with the help of AI. A conversation between the assistant and the patient is recorded and transcribed on-the-go using IBM Watson Speech-to-Text service and then is handled by Health Navigator’s Natural Language Processing (NLP) engine to estimate the patient’s acuity and identify chief complaints. This approach results in proper patient care, as high acuity patients can be handled bypassing the system, and a thorough decision support system provides relevant questions ensuring that a complete clinical picture is formed.Link to the project
Creating robust and economically viable state-of-the-art monitoring toolsets is a challenge across industry 4.0 companies, which use “smart factories” and the Internet of Things (IoT) to create virtual copies of physical environments to enable decentralised decision-making in real-time.
So DataArt, a global technology consultancy that designs, develops, and supports software solutions, is partnering with MHS, an integrator of intelligent material handling systems. DataArt enables the process by deploying cloud-based communication between sensors and gateways connected to conveyor equipment in order to store, analyse and visualise data, detect anomalies and trigger alerts. It uses a suite of technologies to build scalable, accessible, and cost-efficient solutions.Link to the project
Anton Dolgikh, head of AI at DataArt considers whether we are ready for artificial radiologists, and their mistakes, as solutions are sought to lighten the load on the workforce. “We are at a crossroads,” these are the words that have become synonymous with the healthcare revolution over the last few years. During this time, advancements in Machine Learning-based image processing have reached impressive heights. A quick glance at the latest news and you will be bombarded with headlines like ‘AI generates faces of non-existing people.’ Yet, the news from the medical ground is less positive and laden with phrases that start with “understanding and confronting our mistakes…” There are a great many articles concerning medical image processing which state the number of scans per patient has grown dramatically over the last couple of years, and so too has the burden on radiologists. What we need is an unequivocal and sleepless assistant to come and save the situation. But are we ready for artificial radiologists? And better yet, are we ready for their mistakes?Link to the project
Peter Vaihansky, SVP at DataArt, speaks with Jill Malandrino, Global Markets Reporter at Nasdaq, about operational and technological challenges surrounding AI and ML, such as transparency of algorithms, integration into business environments, data processing, and talent shortage.Link to the project
Your message has been sent