Artificial Intelligence (AI).
Create intelligent solutions that will propel your business’s development.
At DAC.digital, 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.
Our areas of expertise in the AI development.
Optimization is one of the applications of AI where the benefits are immediately visible and measurable. Our team of experts with vast experience in working with emerging technologies can help you with a tailored optimization solution.
Explore these AI solutions below for inspiration, or if you have other idea, we’re here to help you bring it to life:
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. Planning and scheduling help to determine decision-making actions performed by robots or computer programs to achieve a specific goal.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.
There are several compelling reasons why investing in AI development is highly beneficial. Firstly, increased automation can significantly speed up and optimize processes, such as decision-making or assembly, while concurrently reducing operational costs. Furthermore, AI methods have the potential to enhance the accuracy and precision of specific tasks currently performed by human operators. Lastly, the ongoing development of AI methods yields unforeseen outcomes, and early adoption and research investment in this domain could mitigate the risk of falling behind competitors and gaining a competitive edge through novel intellectual property.
Computer Vision is a branch of deep tech that adapts AI for multiple tasks and project stages.
Here are exemplary technological problems in which we can help you:
Environmental awareness for autonomous systems
Multi-camera assets monitoring
3D localisation and movement analysis of body parts
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 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.
Automated multilanguage translations: 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.
You can achieve it via hyperparameter optimisation. It allows the models to perform generalisations to operate effectively with training and new data inputs.
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.
L1 and L2 regularisation add a penalty to the loss based on the model’s parameters. Regularisation encourages simpler and more generalised models.
Model parallelism and distributed training allow parallelising computation and workload sharing. These techniques enable faster training and inference times in AI modelling.
Natural Language Processing (NLP)
- 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
NLP includes the following models and tasks:
Text understanding and text generation via generative AI
Answering questions, chatbots and virtual assistants
Does OpenAI take the responsibility 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:
MedTech, healthcare and sports
- Satellite data analysis and processing
- Edge computing
- Autonomous vehicles and factories
In my opinion, in the upcoming years there will be a switch to locally run models without the need to communicate by API to external ‘huge trained model provider’ like OpenAI. Every commercial and academic entity would like to host their own multipurpose models (probably on some kind of VPC (Virtual Private Cloud)) optionally entangled with local edge devices to do analytics. This will be motivated by mainly security aspects and would need advancements in model training and inference optimization process.
Our team of experts.
As a leading service provider of emerging technologies and artificial intelligence development company, DAC.digital’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, PhD
Head of Research
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.
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 Researcher
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 Researcher
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
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.
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.
Krzysztof Wołk, PhD
NLP Scientist/Technical Project Coordinator
Natural Language Processing Expert with PhD in the field of Artificial Intelligence. Experienced in AI related project management. Constantly developing. Very interested in dialog systems, human computer interaction, multimedia and signal processing.
Interesting figures and statistics.
Gaze estimation in the wild. Creating a ground-breaking eye-tracking system for in-context market research
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.
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.