Machine Learning (ML) & Artificial Intelligence (AI)




When we combine several technologies we’ll get Artificial Intelligence, whose main mission is to follow to the target. This may be a verification of documents, status checks of devices or creating alerts and reports.


Teaching is the driving force behind the development of this technology – because if we stayed only with simple automation, processes with unpredictable changes would be performed incorrectly. Our solution constantly enriches its knowledge about your business.


When changes occur in the surrounding environment of business processes , Artificial Intelligence (AI) is able to adjust its style of operation to continue to do the job properly.

ARTIFICIAL INTELLIGENCE IS LIKE AN AUTOPILOT – it helps you reach your destination effectively and land safely!

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Automation of processes e.g. resulting from legal regulations e.g. in the area of compliance.

Reporting and intelligent analytics – thanks to which key and important information is extracted from millions of items of information.

Forecasting – an effective business and customer advisor in the field of products and threats, e.g. investment risks.


Back Office – automation of processes from customer response to connection with Know Your Customer solutions.

Customer Experience – detection of problems before they are reported by the Customer – Artificial Intelligence will improve the reception of a service or product.

Detection of violations and unusual behaviours in telecommunications systems.


Anticipation of hardware failures, e.g. in engines – thanks to which the devices are serviced in advance and do not disturb the logistics of work.

Automation of logistic processes – racks equipped with satellite controlled platforms transporting pallets to the right place or self-propelled forklift trucks are just some of the solutions available on the market. Imagine a software that will determine and select the right products to be loaded for transport.

Analyzing processes and learning how to change them over real time – allows you to optimize the costs of work of the devices.


Intelligent M2M – devices gain not only communication, but also the ability to understand each other’s statuses using backup infrastructure.

Failure forecasting – this is an attempt to answer a very important business question: will something stop our service, product? Artificial Intelligence, using sensors and probes, detects and anticipates problems.

Determining the best solutions in operating the process, e.g. inventory optimization.


Intelligent supply chains – smart enough to ensure uninterrupted deliveries even in the event of problems.

Detection of fraud – through data analysis and anticipating of problems we can detect worrying situations and prevent them more quickly.

Automation of processes that are variable, e.g. in terms of customer service, logistics, without the need to implement new software each time. Artificial Intelligence adjusts the process to the task on its own.



Scheme of AI implementation in the organisation


Marcin Mizgalski

I enjoy complex projects handling together with my customers, aiming at innovations, digital transformations, complex (cross-organization) IT services setup. It is my great pleasure at Gfi to develop sales strategy towards new projects acquisition within IoT, Blockchain, Telecom industries, as well as the other vertical markets. Geographical coverage: international projects, mostly within Western Europe.

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Artificial Intelligence (AI) in real project

What is Artificial Intelligence (AI)?

Machine Learning (ML)

Methodology of teaching software (including machines that use it) how to react to a given situation so that they can work automatically. This process is time-consuming and very dependent on the group dealing with the project.

Process Automation (PA)

Automation of processes through the use of software and devices that take over repetitive activities and perform them independently. We distinguish such branches as BPA – Business Process Automation or RPA – Robotic Process Automation.

Robotic Process Automation (RPA i RPA II)

Robotized automation of (business) processes, where the software executes repeated processes – tools of this type usually operate at the level of the user interface, so their implementation does not require changes in business applications. This software is extended by machine learning, which allows it to adjust itself in time to changing business processes.

Process mining (PM)

Exploration of processes necessary to improve the quality of RPA tools is based on the construction of process models for checking and expanding them on the basis of various events recorded in the past in relation to business processes.

Deep Learning (DL)

Deep learning, which is one of the branches of artificial intelligence, allowing to learn about its changes in the environment and adapt to it, e.g. when recognizing objects in photographs. Compared to machine learning, deep learning is usually an independent process of the software itself.

Design by Data (DbD)

Use information collected from quantitative and qualitative sources to determine how business decisions are made for a given set of products, services or customers.

Predictive Maintenance (PdM)

Collecting data on the state of processes and their progress in order to determine the possible failure and its prevention before it physically happens – which of course has a positive impact on the costs of business operations. This issue is often raised in the case of solutions called Internet of Things (IoT), which collect data needed to forecast events.