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DataArt is a trusted technology partner that can help build efficient, automated, and highly accurate systems using modern Artificial Intelligence technology.

We built OCR, KYC, and Computer Vision systems, using both external AI engines and our own research. These technologies empower your business with top-tier Machine Learning technologies, like face recognition, automated document processing, object detection, smart predictive maintenance systems, and more.

We can build custom AI/ML-powered solutions for your business, boost your AI capabilities, or fill data and analytics gaps for companies that do not have the expertise internally, or prefer to rely on an internationally-recognized expert in artificial intelligence and machine learning, like DataArt.

What we do:

  1. Computer Vision SystemsVideo and image recognition and real-time video processing that can be used across industries, from security and KYC, when you need to recognize objects and faces, to healthcare, industrial applications, construction, building inspection, and others.
  2. OCR SystemsWe help you build Optical Character Recognition systems that reduce manual operations by automatically batch recognizing text and images in documents to automate your workflow. This results in lower labor costs and enhanced performance.
  3. Predictive Maintenance and Recommendation SystemsAutomate the decision-making routine and forecast events with probabilistic analysis and user personalization. The technology is widely applicable in many situations, for example in industrial predictive maintenance systems or forecasting systems in finance, energy or healthcare as well as in many other industries.
  4. Data Mining and AnalyticsAdvanced data analytics, clustering, pattern detection and statistical analysis as well as work with Big Data and data visualization. Used for building advanced data sets, reports, and analytic dashboards for various industries.
  5. Natural Language ProcessingText and speech recognition are widely used in modern IT solutions. We can build a voice recognition layer for your application, or an advanced custom chatbot that will intelligently respond to user input and lower the workload for client support without jeopardizing the client experience.

DataArt focuses on delivering end-to-end solutions, starting with the discovery phase and ending with deployment of an ML model and integration into the existing or newly developed client product or environment.

Our engineers work with the most popular modern technologies, including world-class cloud-based MLaaS solutions and classic or deep learning open source libraries.

Our Key Technology Partners in AI/ML


Technologies We Are Experienced Working With


Proof Of Concept Demos:

Our Approach

DataArt’s approach always starts from deep knowledge of business and user needs, which facilitates our understanding of business objectives and allows us to create the technological processes to achieve these objectives.

Business Understanding

Data Acquisition & Understanding

  • Building data pipeline
  • Setting up environment
  • Data wrangling, exploration & cleansing


  • Feature engineering
  • Model training
  • Model evaluation


  • Scoring
  • Performance
  • Monitoring
  • Support

How We Work

The benefit in working with us derives from our ability to deliver cost-effective and value-enhancing solutions. That’s why we’ve developed an approach to R&D projects that allows us to see the progress at every stage of the project and deliver solutions incrementally. The client can determine at each stage whether additional effort is worth investment or if a change in direction is necessary.

Phase 1.1: 2–4 weeks

Feasibility study

  • Choose most appropriate dataset, model and model parameters
  • Prepare a ML model for a simulation with production data
  • Elaborate on a suitable integration approach
Phase 1.2: 1–3 months

Building PoC

  • Research applicable datasets in terms of data volume and set of fields; create ETL
  • Test different ML models, algorithms, and libraries
Phase 2: Duration depends on the project

Going live

  • Prepare and integrate a production-ready ML model
  • Optimize and improve the model with new production data, weights, parameters
  • Improved model rollout
Phase 3

Feasibility study

  • Support
  • Effectiveness monitoring