Machine learning
and AI

Even though AI was founded more than 60 years ago, it has exploded very recently. This mostly has to do with the emergence of powerful hardware, cloud and mobile technologies that allow handling and processing of Big Data.

Lemberg team leverages these opportunities to develop Machine Learning (ML) algorithms that turn Big Data into meaningful information for the end users. Reach out to us to learn how your product can generate exceptional value powered by AI.

Our expertise

Our data science team combines excellent engineering skills and academic knowledge in machine learning and artificial intelligence.

Sensor data processing to track and analyze object behavior and location (e.g. people, animals, transport).


Sound recognition to process and analyze voices and sound patterns, and detect unusual or undesirable sounds in the environment.


Computer vision to locate, track and analyze object appearance and behavior on photo and video.

Natural language processing to build chatbots, content recommendation engines, and other tools that analyze a text in one or several languages.


Data science team

Experience in consumer, industrial and medical projects

Took part in academic and commercial research

Own tool-set for data analysis and cleanup


Data science team

Machine Learning team

How we work

Frame the problem
Identify your key goals and challenges.
Collect data
Collect data to address the identified goals and challenges.
Evaluate data quality
Check the collected data to see how it can be used in building and training the information model.
Preprocess data
Clean and structure the data as necessary to improve future training accuracy.
Design a machine learning algorithm
Create a machine learning algorithm to achieve your goals.
Deploy the information model
Wrap the algorithm in a library and integrate it with your product (e.g. app, device, website).
Assess the results
Continuously assess the model performance and improve the algorithm when necessary.
What you get
Research report

Brief information on the experiments carried out during the research, their methodology, the result obtained and their interpretation


Implementation of developed algorithms in form of Prototype

Informational model

A model obtained as result of data processing with machine learning algorithms in form of neural network, configured fuzzy logic controller, mathematical formula etc.

Algorithm correction

Tuning and optimizing of algorithm for better performance

Collected datasets

Data sets contain the value of informational parameters of the modeling object and are used to construct and validate an information model of object or process.

Field test report

Report with results of comprehensive Field testing of the prototype and their interpretation

Algorithm description

Description of the principle of the functioning of solution

Description for patent registration

For the know-how found during the research, technical documentation for the registration of a patent for a utility model may be prepared.

Contact us

Please tell us more about yourself and your project.

Lou Dutko
Chief Technology Officer
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