Artificial intelligence has many variations. Our experts will help you find the one that fits your business needs.

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Artificial Intelligence

Artificial intelligence is the most widely recognized technical term for robotics and automation. It’s also one of the most influential digital fields in the world. AI refers to the process of machines simulating the human brain, which requires developers to create artificial neural networks that can mimic logical reasoning, learning, and self-correction.

How You Can Use AI:

  • Planning and decision making
  • Multi-agent systems
  • Optical Character Recognition (OCR)
  • Data mining and information retrieval
  • Computational neuroscience
  • Human-computer interfaces

Artificial intelligence is all about decision-making and acting based on real data. AI can perform tasks previously done by humans both faster and more accurately.

Natural Language Processing

Natural Language Processing (NLP) is a subfield of Machine Learning. The technology processes written or spoken language, deriving details like a sentence’s meaning or context and then generating an output based on that. NLP combines computational linguistics with statistical, machine learning, and deep learning models.

How you can use NLP:

  • Spam Detection
  • Machine Translation
  • Virtual Assistants and Chatbots
  • Social Media Sentiment Analysis
  • Text Summarization

Businesses use NLP to analyze client feedback and automate customer service. You can deploy it to free up more time to focus on your customers, boosting conversion and loyalty as a result.

Machine Learning

Machine learning is a field of computer science and a subset of artificial intelligence. It focuses on teaching computers how to learn through experience and then perform a task without being explicitly programmed for that task.

Machine learning systems become more efficient over time, delivering more accurate, consistent results the longer you use them.

How you can use ML:

  • Fraud Detection
  • Spam Filtering
  • Predictive Maintenance
  • Propensity Prediction
  • Recommendation Engines
  • Malware Threat Detection
  • Business Process Automation

Businesses use machine learning to analyze massive, complex data sets and deliver quicker, more accurate results. You can harness the technology to transform your company into a data-driven organization powered by automated processes and scalable solutions.

Deep Learning

Deep Learning is a machine learning subfield that constructs artificial neural networks to simulate the structure and functions of the human brain. The adjective “Deep” refers to more complex models (harnessing millions of parameters) instead of shallower neural networks. 

How you can use Deep Learning:

  • Speech Recognition
  • Money Laundering Detection
  • Image Caption Generation
  • Text Generation

Deep learning enables businesses to use structured, semi-structured, even unstructured data to perform tasks. The technology can also spot more complex patterns in data, resulting in a more efficient data-driven decision-making process.

Neural Networks

Neural networks are a subset of Machine Learning and are at the heart of Deep Learning algorithms. They reflect the behavior of the human brain, mimicking the way biological neurons signal one another, enabling computer programs to identify patterns and solve common issues in the fields of AI, Machine Learning, and Deep Learning.

How You Can Use NN:

  • Consumer behavior predictions
  • Personalizing the buyer’s experience
  • Demand Forecasting
  • Fraud detection
  • Sales Forecasting

Neural networks are vital in business because they can extract meaning from inaccurate or complex data, finding patterns and trends that would be too subtle for humans or other computer techniques to spot.

Computer Vision

Computer vision can use deep learning, but it also works with standard non-deep learning methods. It enables systems to derive meaningful data from digital images, videos or other visual inputs. Systems can then take action or make recommendations based on the available information.

How you can use Computer Vision:

  • Self-driving cars
  • Facial Recognition
  • Real-time Monitoring
  • Medical Diagnosis

Computer vision completes repetitive tasks quicker, offloading work from employees by automating tasks. What’s more, some deep learning models can achieve super-human performance: they’re better at some jobs than people.

RPA 2.0

Robotic Processing Automation (RPA) 2.0 is the perfect way to relieve employees of tedious, repetitive tasks. It uses machine learning in the decision-making process in place of people, getting a robot to make the final choice, with humans verifying it (and only if necessary).

How you can use RPA 2.0

  • Payroll Processing
  • Shipment Scheduling and Tracking
  • Vendor, Customer, and Employee Onboarding
  • Report Aggregation

RPA 2.0 helps organizations work more efficiently by automating processes and eliminating human error. Use it to give your team the space to focus on creative work, leaving mundane tasks to machines.

We’ll find the best technology for your business

Our AI experts will help you complete a full-cycle AI development project, turning ideas into optimal solutions. We’ll help you define your goals, find a suitable technology, and build an AI solution that continues to deliver value, year after year.

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Filip Skurniak

Head of AI

Worked at Samsung R&D Center in Poland leading research on NLP deep learning algorithms and implementing scalable cloud solutions. He has experience in research and commercial projects including building deep tech products from research idea to productionisation. Striving to provide business value first and avoid waste. Co-creator and patent holder of the global TCL smartphones’ gallery app AI feature.

Maciej Karpicz, PH. D

Chief Technology Officer

Maths PhD who loves using mathematical models to perfect custom AI solutions. He’s highly experienced in systems architecture and AI development, using his knowledge in his role as CTO of DiabetesLab to take forward Suguard: a mobile app that uses Machine Learning, AI, and Data Science to help people with diabetes. Also CTO of DLabs.AI, using AI and Machine Learning to help businesses optimize workflows and drive efficiencies.

Tomasz Maćkowiak

Machine Learning Engineer

A software development veteran who’s worked on Python applications since 2007. He started with backend and frontend web application development, took an interest in Machine Learning in 2017, and then started working on it full-time in 2019. Neural Networks are his ML tool of choice. And thanks to his background in software development, he’s more focused on good coding practices than your average Data Scientist.

Tomasz Iżycki

Data Scientist

Machine Learning expert with solid software development standards, specializing in time series prediction. Single-handedly delivered complex ML models to support the decision-making processes of 8,000 convenience stores, starting with nothing more than a client’s idea. Also less experienced in but fully dedicated to Deep Learning and Computer Vision.

Emilia Brzozowska

Engineering Manager

Engineering Manager with over four years of commercial experience in Data Science, Machine Learning, and R&D, and more than one year of project management experience. She has strong knowledge of predictive analytics and recommendation systems. Also adept at Natural Language Processing and Computer Vision. She attaches great importance to communication with the client and always adheres to software development best practices. Focused on achieving business goals and being a liaison between the development team and the client.

Technologies we work with