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Artificial Intelligence (AI).  

Create intelligent solutions that will propel your business’s development.



At, we believe in the power of artificial intelligence and the ability it brings to different aspects of everyday life and business. Artificial intelligence research and progress has gained momentum in recent years. As an artificial intelligence software development company, we are ready to help you take your business to the next level by using different research areas in this branch of computer science.

What’s the difference between
AI and ML?

It’s an important question to answer. Although artificial intelligence and machine learning are sometimes used as a synonym, these two are still different technologies. Artificial intelligence (AI) is a broad concept that entails creating intelligent machines that simulate human intelligence, language, thinking capability and behaviour. It is mainly used to perform tasks commonly considered repetitive and tedious.

Machine learning, on the other hand, means applying a specific type of AI or system (AI systems) that allows the machine to learn from the provided data without explicit programming. In other words, AI does not require pre-programming. It uses algorithms that work with their intelligence. Machine learning means acquiring knowledge through data.

In short, AI is a broad concept, and ML narrows it down to the specific needs of specific tasks. It uses a subset of AI, for example, automatically allowing a machine to learn from past structured and unstructured data.

Although some predictions say that artificial intelligence could function independently, it will require human intelligence to function and progress in the foreseeable future.


Our areas of expertise in the AI development software.

We can provide dedicated experts and expert systems in emerging technologies. Term artificial intelligence is not limited to one area but is an integral part of other fields, including computer vision and machine learning which we will gladly help you with. Let’s review some examples of AI use that are important and popular.

Optimisation techniques

The choice of an optimisation method depends on the specific AI model, dataset, and optimisation goals. This AI field is constantly being researched and improved. Here are some things you can achieve with artificial intelligence optimisation methods:

Identify the best configurations of a model for the current needs: you can achieve it via hyperparameter optimisation. It allows the models to perform generalisations to operate effectively with training and new data inputs.

Minimise the loss in the model: gradient descent calculates the gradients of the loss function concerning the model’s parameters. The algorithm adjusts the parameters toward the steepest descent to find the optimal values. The stochastic gradient descent variant randomly selects a subset of training samples for gradient estimation. It’s widely used in large-scale training and deep learning, as it helps to reduce computational costs and speeds up convergence to boost performance.

Prevent overfitting of the model: L1 and L2 regularisation add a penalty to the loss based on the model’s parameters. Regularisation encourages simpler and more generalised models.

Divide the model across different devices or machines: model parallelism and distributed training allow parallelising computation and workload sharing. These techniques enable faster training and inference times in AI modelling.

Naturally, there are more methods for optimising AI-based solutions. The best way to find the most suitable one is to define the needs of your AI or ML project and then tailor the best optimisation plan. Practical uses of optimisation techniques include:

Multi-vehicle routing for transportation

Planning for a fleet of vehicles is a process that maximises the revenue from the available resources but has to consider numerous constraints like the maximal permissible amount of working hours of drivers, different fuel and fees among countries, as well as the potential to take an additional parcel when the truck is not fully loaded. Automating this planning can benefit the company, ensuring both short- and long-term benefits.

Price optimisation for travelling

Travel agencies often have to find the golden mean between their margin and the client’s willingness to pay. An algorithm that considers historical data and the data retrieved in real time about the available offers could be used to maximise the margin dynamically, assuring at the same time that it won’t be lower than a certain threshold.

Energy demand-side load balancing

The interconnection of various energy consumers in a common grid raises the problem of limited energy efficiency of the providers, varying energy prices, and the limited storage capacity of local magazines. Therefore, an appropriate load balancing should be implemented to manage the demand-side energy allocation.


Planning and scheduling

Automated planning and scheduling processes can be beneficial for a range of projects. It comes with its challenges and limitations. However, ultimately it’s good to look into it to see how it can help your process or product. Planning and scheduling help to determine decision-making actions performed by robots or computer programs to achieve a specific goal. A good planning and scheduling system requires domain description, task specification, and goal description. A plan involves a sequence of actions, each of which has its preconditions that must be met before it can act and some effects that can be either positive or negative.
Good planning and scheduling systems can contribute to different business aspects, such as:

    • Improving efficiency

    • Enhancing decision-making processes by deductive reasoning and other methods

    • Real-time adaptability

    • Resource optimisation

    • Upscaling and downscaling

    • Data-driven insights

You can use different types of planning and scheduling algorithms to achieve different results.

Backward state space planning (BSSP)

Also known as backward planning or goal-driven planning, this AI planning system is another method that searches through state space. Contrary to the FSSP, BSSP starts from the goal state and systematically reasons backwards to determine the actions and states needed to reach that goal.

Backward state space planning involves reasoning backwards from the goal state, determining the necessary actions and states to achieve that goal, and constructing a plan or solution that satisfies those requirements. It is a practical approach in various planning and problem-solving applications within artificial intelligence.

Forward state space planning (FSSP)

FSSP behaves similarly to forward state space search. With an initial state S in any domain, we perform some necessary actions and obtain a new state S, which includes some new terms. This is an example of a progression process.

Forward state space planning involves systematically exploring a problem’s state space, considering actions and their consequences, and searching for a sequence of actions that leads from an initial state to a desired goal state. It is fundamental in many planning and problem-solving applications within artificial intelligence.


Natural Language Processing (NLP)

This subfield of AI development services deals with the interaction between computers and human language. It involves tasks like developing algorithms and techniques to enable computers to understand, interpret, and generate language in a meaningful and valuable way. NLP algorithms and models are built upon linguistic principles, statistical methods, and machine learning techniques
It includes the following models and tasks:
  • Text understanding
  • Sentiment analysis
  • Machine translation
  • Answering questions
  • Chatbots and virtual assistants
  • Text generation via generative AI
NLP-based AI development solutions can help program AI to contribute to the business in the following ways:
  • Customer insight and feedback analysis
  • Enhanced customer support
  • Information retrieval and knowledge management
  • Automated document processing
  • Market research
  • Multilingual communication
  • Content generation
  • Risk analysis and compliance

Conversational AI, speech AI and generative AI.

These three types of artificial intelligence have especially gained momentum in recent years.

Conversational AI

It’s widely used in chatbots on websites and as separate applications. However, it can involve multiple communication channels, while chatbots only employ text. It can also generate real-time responses and better understand the user’s needs than chatbots based on keywords and static answers. One of the most prominent users of this type of AI involves customer care and social media management. Conversational AI can find its use in the following:

  • Human-like chatbots for customer care: can help handle complaints, provide general information about the company and status reports and integrate with external systems.
  • Salesbots: those bots can generate an offer for the client based on the project history and describe past projects in the desired category.
  • Virtual assistants can help in everyday matters like ordering food or deliveries, making reservations and purchases, etc.

Real-time interpreting and translations: an intermediary system that can translate the textual information exchanged between different users in real-time, prepare text summarisation and reformulation if needed (e.g. summarise the main outcomes of the meeting; what was the main topic of a call)

Speech AI

It can contribute to managing documentation and customers. It can add a self-service option, automating some of the processes. They also offer good scalability options and can enhance experiences by reducing hold times and offering immediate assistance with more straightforward methods where human intervention is optional. It can be used for:

  • Speech enhancement: could help you sound more professional and be more understandable. At the same time, it could also be used to understand better what the client is saying, especially when the sound quality could be better, or the client’s pronunciation could be more understandable.
  • Real-time meeting captioning: it could be implemented in conference rooms to take notes from a meeting and provide textual information to people remotely connected to a conference room, especially when the sound quality is poor.
  • Expressive voice bots for customer care: automated customer care is often repellent due to a strongly mechanical-sounding voice, and customers try to reach a human being. On the other hand, human-made recordings have limited capabilities in terms of cases which can be served. Therefore, a human-sounding voice bot could remedy the abovementioned issues, ensure a lower rejection rate at the beginning of the conversation, and efficiently offload human operators.

Generative AI

It can produce realistic and seemingly original content. From short texts, poetry and books to artworks and other types of content. It can be helpful to create bulk items for companies that require a lot of graphic design or written content. It can be ideal for writing news articles, blog posts, and stories.

Uses for generative AI include the following:

  • Automated multilanguage transactions: a tool that can take a textual input and translate it automatically into multiple languages. This can be used to prepare human-like translations of entire websites, documents, or conversations with others, opening new business opportunities and reducing overall costs and timelines related to translations.
  • Personal design assistant: it can take as input text or sketch and propose designs that align with the provided guidelines. Can prepare alternative designs, fill in the missing parts of the image or remove specific objects from images.
  • Taking notes: extracting crucial information from long texts related to a specific subject as bullet points can serve as a time-saver and minimises the risk of overlooking important detail from emails, descriptions of products or meeting transcriptions.

Does OpenAI take the responsibility for ChatGPT abuse?

We asked this question ChatGPT itself. Check it and read’s opinion on it by downloading this document:

    What level of responsibility does OpenAI take for ChatGPT abuse?




    Are you curious about the level of responsibility that OpenAI bears for ChatGPT misuse? Are you concerned about generative AI technology’s potential security and business consequences?

    Look no further!




    Industries, we use AI-powered solutions in:

    Space & defense


    • Satellite data analysis and processing
    • Edge computing
    Medical Imaging and Robotics


    • Autonomous vehicles and factories

    Manufacturing optimisation

    Medtech, Healthtech & Sport

    MedTech, healthcare and sports



    Computer Vision

    AI and Computer Vision.

    Computer Vision is a branch of deep tech that adapts AI for multiple tasks and project stages. The practical uses of Computer Vision include:

    Environmental awareness for autonomous systems

    Multi-camera assets monitoring

    3D localisation and movement analysis of body parts

    Our team of experts.

    As a leading service provider of emerging technologies and an artificial intelligence development company,’s team of experts is at the forefront of AI technology development and its business application. We follow a meticulous and systematic approach that makes the journey fruitful and productive for both parties.

    Our clients interested in AI solutions can benefit from free discovery workshops conducted by our in-house experts. They also help identify and aid in creating appropriate solutions and roadmaps for achieving your technical goals.

    Marek Tatara, PhD

    Head of Science

    Assistant Professor at Gdańsk University of Technology, AI/ML Expert at M5 Technology, Member of the Polish Society For Measurement, Automatic Control And Robotics. works on the company’s research agenda and works on the implementation of both EU-funded and commercial R&D projects from the field of Computer Vision, Machine Learning and Embedded Systems.

    Stanisław Raczyński

    Chief of Innovation Officer

    A distinguished professional with an impressive track record of 17 years in ML/AI and audio DSP research and 23 years of engineering experience. He has actively contributed to various applied research projects, demonstrating his expertise in signal processing, natural language processing, machine learning, and robotics.


    Machine Learning Researcher

    Graduated from the Faculty of Electronics, Telecommunications, and Informatics at the Gdansk University of Technology. He is interested in applying synthetic datasets for learning deep neural networks and in learning algorithms to reduce the amount of data required for effective network training.

    Łukasz Brandt

    Senior Security Analyst

    His main professional interests include the security of IoT systems, Systems of Systems (SoS), AI/ML security issues, cyber immunity in the context of cyber-physical systems (CPS), digital transformation and Industry 4.0/5.0. He has extensive experience in analytical and design work in the area of network, hardware and software security as well as threat, risk and vulnerability modelling.


    Machine Learning Researche

    Graduated from the Faculty of Electronics, Telecommunications, and Informatics at the Gdansk University of Technology. He is interested in using deep learning in biomedical engineering and generating synthetic data such as photos and texts


    Machine Learning Researche

    Graduated from the Faculty of Electronics, Telecommunications, and Informatics at the Gdansk University of Technology. His areas of interest mainly focus on computer vision tasks, including biomedical data processing and deep neural network training and evaluation. Apart from that, he also enjoys web development topics

    Michał Affek

    Embedded Machine Learning Researcher

    Currently enrolled in an industrial PhD programme at the Gdansk University of Technology. His main interests are remote sensing (processing done specifically on satellites), machine learning algorithms for edge devices, and parallel computing.

    Michał Ostyk

    Computer Vision Engineer

    A Computer Vision Engineer with experience in agriculture, fast food, sports analytics, and healthcare. He loves researching SOTA and converting it into an MVP in Pytorch. However, he recently delved deeper into MLops.

    Interesting figures and statistics.

    The global market size of AI, currently in 2022, is estimated to be USD 387.45 billion, and it is expected to grow by USD 1,394.30 billion by 2029. Nearly 2/3rd of companies are deploying AI solutions to have a competitive edge in the market.


    Compound annual growth in market size till 2029


    The potential contribution of AI to the global economy by 2030


    using AI believe their work becomes easier

    Our solutions.

    Check out the examples of the solutions we built for our clients in the artificial intelligence development software department.

    Indulge yourself in the latest insights.

    We’re professionals who are also endlessly curious tech enthusiasts. See our recent blog posts related to research and the use of AI about the trends and artificial intelligence for software engineering.

    Trust us to guide you through your next AI project.

    Are you working on a research project and would need some automation? Or maybe you’ve already done the research and are looking for a team of experts to aid in creating that perfect solution. We’re here to help. Please tell us what you need, and we’ll see how we can help you achieve your goals and clarify the process.

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