Technologies we work with

Cloud Infrastructure

We provide cloud infrastructure design, implementation and maintenance services.

What is cloud infrastructure?

Cloud infrastructure is a collection of components and elements needed to deliver cloud computing. It includes computing power, networking, storage and an interface that allows users to access virtualized resources.

Virtual resources mirror those of the physical infrastructure and include components such as memory, network switches, servers and storage clusters. They are needed to create applications that users can access via the cloud or retrieve via the internet, telecommunications services and wide area networks (WAN). The cloud infrastructure approach offers benefits such as greater flexibility, scalability and lower cost of ownership.

A cloud infrastructure allows organizations to access their data storage requirements and computing capabilities when they need them. Instead of building on-premise IT infrastructure or leasing data center space, organizations can now lease their cloud infrastructure and computing capabilities through third-party providers.

Cloud infrastructure is available for private, public, and hybrid cloud systems. It can also be leased through cloud providers and through several cloud infrastructure delivery models.

How does cloud infrastructure work?

Cloud platforms and infrastructure work through an abstraction process, such as virtualization, to separate resources from the physical hardware on which they are typically installed in the cloud. These virtual resources are provisioned in cloud environments using tools such as automation and management software, allowing users to access the resources they need, when they need them.

What is fog computing?

Fog computing is an extension of the cloud, which brings processing power, storage and networking closer to the end devices (edge/IoT).

Basically, data no longer has to be sent all the way to a central cloud datacenter (which can be very far away), but is processed locally or in intermediate nodes (gateways, smart routers, micro-servers).

Key features

• Proximity – processing is done close to the data source.
• Low latency – faster reactions for real-time applications (autonomous cars, smart factories).
• Bandwidth savings – no longer send all raw data to the cloud, only what is relevant.
• Better security – sensitive data can remain local.

Simple example

• Without fog computing: A traffic sensor sends all the data to the cloud → there it is analyzed → the answer comes back (may take a few seconds).
• With fog computing: The data is analyzed directly by a local gateway → the decision is made instantly (milliseconds) → only the summary reaches the cloud.

Fog computing = "fog" between the cloud and the edge devices, which process the data closer to their source.

Edge Computing

Edge computing = processing data directly at the edge of the network (edge), that is, on devices or equipment very close to where the data is generated (sensors, cameras, machines, smart routers, etc.), instead of sending it all to a central cloud.

Features

• Maximum proximity – data is processed right on the device or in a nearby node.
• Very low latency – near real-time response.
• Reduced traffic – only the results or processed data go to the cloud, not all the raw data.
• More local control and security – data does not leave the area where it is collected, if not needed.

Examples

• Autonomous cars – process data from sensors and cameras directly in the car, without waiting for a response from the cloud.
• Smart medical devices – monitor the patient and make decisions quickly, even if there is no good internet connection.
• Smart factory – robots and sensors coordinate their movements through local computing, without delay.

Difference from fog computing

• Edge computing = processing is done directly on the source device or very close to it.
• Fog computing = processing is done in an intermediate layer (e.g. gateways, local micro-datacenters) → between the edge and the cloud.

Edge computing = you bring the "intelligence" right next to the sensor/device, for speed and autonomy.

Network Functions Virtualization NFV

NFV = Network Functions Virtualization.

It is a technology through which network functions that traditionally ran on dedicated hardware equipment (routers, firewalls, load balancers, VPN appliances, etc.) are virtualized and can run on regular servers (x86, blade servers), in the form of software.

How NFV works

• Instead of buying a physical firewall or a dedicated router, you have a software application (VNF – Virtual Network Function) that runs on a server.
• Several such functions can be hosted on the same hardware resources through virtualization.
• Everything is orchestrated and managed through management platforms (e.g. OpenStack, VMware, Kubernetes for containerized functions).

Examples of virtualized network functions

• Firewall
• Virtual router
• Load balancer
• NAT (Network Address Translation)
• IMS / VoIP core functions (in telecom)

NFV advantages

• Cost reduction – you no longer need expensive hardware.
• Flexibility – you can quickly launch a new network function through software.
• Scalability – resources can be dynamically increased or decreased.
• Automation and integration – functions can be managed together with SDN for intelligent networks.

In short: NFV = moving network functions from dedicated hardware → to software running on virtual servers.

Difference from SDN

• SDN = separates and controls network traffic through software.
• NFV = replaces network hardware with virtual software applications.

Software Defined Networks SDN

SDN = Software-Defined Networking.

It is a modern approach to computer networks, where the control of traffic and network equipment (switches, routers, etc.) is no longer done manually, through hardware configuration, but centralized and automated, through software.

How SDN works

• Separates the planes:
  
  • Control plane (routing and traffic decision logic) – moved to a central software controller.
  • Data plane (the part that actually moves packets through the network) – remains on the equipment.
• The SDN controller manages the entire network and sends instructions to the equipment.
• Administrators can manage the network through a software interface (API, dashboard) instead of configuring each switch/router manually.

SDN Advantages

• Automation – changes are made quickly, through software.
• Scalability – easy to adapt when new servers or applications appear.
• Security – access policies can be applied centrally and uniformly.
• Cost reduction – less manual work, efficient use of resources.
• Agility – the network responds faster to the needs of applications and users.

In short: SDN = software-controlled networks, more flexible and intelligent than traditional ones.

Internet of Things IOT and IIOT

IoT stands for "Internet of Things"

In short, it is a network of physical objects – devices, machines, sensors, appliances – connected to the internet, which can collect and exchange data with each other, without always requiring human intervention.

Simple examples of IoT

• Home: a smart thermostat that automatically adjusts the temperature; a smart fridge that sends a notification if you run out of milk.
• Transportation: connected cars that send data about traffic or engine status.
• Health: smart watches that monitor your heart rate and send data to your doctor.
• Smart cities: street lights that only turn on when they detect movement or dim to save energy.

IIoT = Industrial Internet of Things

It is the application of the IoT concept to industry and manufacturing, to connect equipment, sensors, robots and control systems to the internet and to each other, so that they can collect and share data.

IIoT Features

• Industrial connectivity – machines and sensors in factories or plants are interconnected.
• Real-time data collection and analysis – for performance, preventive maintenance and process optimization.
• Automation and intelligent control – systems that can make autonomous or semi-autonomous decisions.
• Industrial security – protection of data and critical equipment.

IIoT examples

• Sensors on factory machines that monitor wear and tear on parts and send alerts before failure.
• Energy control systems in power plants or factories that optimize consumption.
• Connected industrial robots that communicate with each other for coordination and efficiency.
• Real-time supply chain monitoring through sensors and RFIDs.

In short: IIoT = Internet of Things applied to industry, for smart equipment, real-time data and process optimization.

The difference between IOT and IIoT (Industrial IoT) is that IoT is more applicable to everyday life and consumer tech, while IIoT is oriented towards industry and production.

AI / ML, Deep Learning

Artificial Intelligence (AI)

Artificial Intelligence is the broad field that seeks to create systems capable of performing tasks that require human-like intelligence.

AI focuses on the ability of machines to reason, learn, make decisions, and solve problems.

Includes rule-based systems, symbolic reasoning, natural language understanding, robotics, computer vision, and more.

Example: A chess program that uses logic and strategy to defeat human players.

Machine Learning (ML)

Machine Learning is a branch of AI that uses data-driven methods.

Instead of being given explicit rules, algorithms are trained with data to learn patterns and make predictions or decisions.

They are based on mathematical models that automatically improve with experience.

Example: A spam filter that increases its accuracy as it analyzes more examples of spam and non-spam emails.

Deep Learning (DL – Deep Learning)

Deep Learning is a specialized branch of Machine Learning.

It is based on artificial neural networks with multiple layers (“deep structures”).

These networks automatically discover features directly from raw data, often without human intervention to define them.

It is very strong in image recognition, voice processing, natural language understanding and generative intelligence.

Example: A voice assistant that understands spoken commands and generates human-like responses.

In short:

• AI = big umbrella (intelligent behavior).
• ML = data-driven learning methods, within AI.
• DL = neural network-based learning, within ML.

Artificial Intelligence (AI) in business

• Chatbots and virtual assistants → 24/7 customer support.
• Predictive analytics → forecasting sales and market demand.
• Intelligent automation → time reduction for repetitive processes (e.g. document processing).
• Personalized recommendations → offers tailored to each customer.

Machine Learning (ML) in business

• Fraud detection → identifying suspicious transactions in banks and fintech.
• Customer segmentation → grouping customers based on purchasing behavior.
• Price optimization → dynamically adjusting prices based on demand and competition.
• Churn analysis → predicting customers who may abandon the company's services.

Deep Learning (DL) in business

• Visual recognition → image analysis for retail (e.g. inventory monitoring via cameras).
• Document analysis → automatic extraction of information from contracts and invoices.
• Sentiment analysis → understanding customer opinions from reviews and social media.
• Content generation → automated ads, marketing texts, design.