Europe Union

for Manufacturing & Warehousing.

Modernise your supply chain with Artificial Intelligence technologies

Modernise your supply chain with
Artificial Intelligence technologies

Our expertise is to provide intelligent, data-driven tools that help streamline and improve various aspects of manufacturing processes. Whether it’s through optimising supply chains, enhancing quality control, or predictive maintenance, our innovation-driven approaches are tailored to meet the specific needs of your manufacturing and warehousing operations. Explore how our expertise in deep technology can assist in making your production more efficient and reliable.

Custom Artificial Intelligence (AI)
development services for the
manufacturing & warehousing industry

How you can use artificial intelligence and other advanced technologies to improve manufacturing processes?

AI and machine learning in robotics

You can enhance your robotic solutions with computer vision, advanced AI systems, and sensors to improve quality and reduce the costs of your operations. The combination of these technologies offers multiple applications, including temperature measurements, monitoring various parameters like pressure, humidity, and energy consumption, and detecting vibrations or gases to identify malfunctions. Predictive analytics helps to utilise sensors and AI systems to monitor the health of robotic equipment and prevent failures before they occur.

Machinery automation

Deep tech solutions help us enhance the machinery by adding automated elements to aid with repetitive tasks and boost efficiency. AI-driven monitoring and control, IoT-enabled machinery, automated quality control and energy consumption optimisation ensured by technologies like AI and computer vision enable significant increases in operational efficiency, with a notable reduction in downtime and energy costs.

Robot manufacturing processes

Artificial intelligence can be a powerful tool for enhancing robot manufacturing. AI algorithms can help design robots by suggesting efficiency, durability, and cost-effectiveness optimisations. These tools can quickly simulate and evaluate countless design variations, identifying the best options before making physical prototypes. AI-supported robot engineering can also aid in building robot sensing mechanisms.

Enhancing supply chain operations

We can leverage AI to enhance your supply chain systems by leveraging advanced technologies in several ways. ML algorithms can analyse the supply chain network, identifying bottlenecks and inefficiencies. We can also leverage AI algorithms to monitor stock levels across various locations and automatically trigger replenishment orders based on predictive analytics.

Automation & enhancement of production processes

Proactive maintenance models can help effectively reduce failures and malfunctions in production processes. Additionally, implemented computer vision systems with ML capabilities to automate the inspection of products for defects. IoT sensor deployment adds an extra layer of data collection that provides insights to build a foundation for process optimisation and predictive maintenance strategies.


Computer vision is an ideal choice for boosting cobot efficiency. Advanced systems allow real-time recognition of tools, parts, and assembly configurations. It allows them to adapt to different tasks on the fly, reducing downtime associated with manual reconfiguration. Machine learning enables adaptive learning that allows cobots to earn from their environment and human counterparts, improving their performance over time.


High-resolution cameras combined with computer vision algorithms are ideal for analysing crop health from aerial images. Integrated ML models trained on vast crop imagery datasets detect pest infestation signs. Utilising AI to process data collected by drones, including plant counts, size, and health indicators, can predict crop yield with high accuracy.

Only 13% of the companies that introduces AI knows how to utilise it for a true innovation.

Be one of them.
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Why should you invest in advanced manufacturing solutions?

Energy and resource management

Reduce energy consumption and operational costs

By analysing energy usage patterns through IoT sensors and AI algorithms, we provide insights and recommendations for energy savings, leading to cost-effective and sustainable operations.

Streamlining production processes

Increase production efficiency and reduce waste implements IoT and AI technologies to optimise production lines. Our solutions enable real-time monitoring and predictive maintenance, reducing downtime and increasing throughput.

Enhancing quality control

Maintain high product quality and consistency

Leveraging advanced data analytics and machine learning, we offer systems that continuously monitor and analyse product quality, detecting anomalies and ensuring consistent output.

Supply chain optimisation

Improve supply chain visibility and efficiency

Our custom software solutions integrate seamlessly into your supply chain management, providing end-to-end visibility and actionable insights for efficient inventory management and logistics planning.

Worker safety and compliance

Ensure workplace safety and regulatory compliance

We enhance worker safety by implementing IoT solutions for environmental monitoring and smart wearables. Our deep tech solutions for the manufacturing industry also help maintain compliance with industry standards and regulations.

Customisation and flexibility

Adapt quickly to market changes and customer demands

Our agile software development approach allows for rapid prototyping and customisation of products, enabling manufacturers to respond swiftly to changing market demands.

Practical applications of robotics for smart manufacturing

    • Quality Inspection: Computer vision systems analyse products for defects, ensuring consistent quality.
    • Assembly: Robots equipped with vision systems accurately assemble components.
    • Packaging: Vision-guided robots sort, arrange, and package products.
    • Palletising: Automated systems stack and organise products on pallets.
    • Machine Tending: Robots load and unload parts from machines, supervised by vision systems.
    • Welding: Vision systems guide robots for precise welding tasks.
    • Painting and Coating: Robots achieve consistent paint application with vision for monitoring and adjustments.
    • Material Handling: Automated systems move materials efficiently, guided by vision.
    • Part Identification and Sorting: Vision systems identify and sort various components.

Enhancing industrial operations with computer vision

  • 2D Vision: Used for inspection, identification, and guidance.
  • 3D Vision: Provides depth information for more complex tasks.
  • Thermal Imaging: For monitoring temperature-related processes.
  • Hyperspectral Imaging: Detects chemical composition.
  • Motion Analysis: For monitoring and improving dynamic processes.
Marcin Połulich
Marcin Połulich General Manager DAC.DeepTech

Call me and let’s talk about AI possibilities in manufacturing!

Whether you just have some questions or are already in the middle of the project – our experts are here to address your needs. We will help you deliver quality products to the market. Click on a button below to book a 30 minute call with me.
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Our clients’ success stories

See our deep tech expertise with manufacturing innovation in action. Learn more about our projects:

Robots orchestration for semi-autonomous warehouses created a solution (Harmony 1.0) for tasks scheduling for humans and robots in a warehouse. It includes tasks monitoring and adjusting the plan, route optimization, coordinating and distributing real time tasks based on capabilities, location, availability.

  • Data preprocessing: data structure set-up, CAD import and post-processing, Input data (daily orders and components) import to a structure
  • Modeling and simulation: preparation of a formal model of the process, setting up the simulation parameters, implementation of a simulator, preparation of different test cases
  • Orchestrator: establishment of a cost function, definition of metrics for evaluation, optimizer implementation (including tests of different optimizers) with rough trajectory optimization, Implementation of the on-demand rescheduling support

Forest mapping with computer vision-extended autonomous drones

  • Autonomous operation of forestry vehicles can be planned at a larger scale only if certain information, like tree health, what kinds of trees are in specific areas, and which diseases and parasites have affected what trees, are available
  • Our development focused on a drone-based solution, where in-forest flight is performed before harvesting, to determine the 3D surface of the forest and localise trees with their characterization (diameter, species, bend)
  • The camera-equipped drone finds obstacles, determines where the trees are and characterises them
  • It collects data from RGB cameras and LiDAR, then fused with available national data to provide the enriched map for operations planning

Smart city IoT platform architecture connecting 20,000 devices

  • Our client needed a solid architecture to operate an IoT smart city platform with a massive number of devices
  • Additionally, they needed a dedicated management system for service requests to limit time and costs while handling requests
  • We designed and implemented a wide-scale resilient solution providing monitoring and management capabilities on a mass scale in a distributed manner
  • We developed an auto-classification of service requests by connecting devices such as traffic light controllers
  • We also provided a mobile application for the service teams to operate efficiently on the field

Meet the technology experts behind the industrial innovations

Marek Tatara, PhD 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 implementation of both EU-funded and commercial R&D projects from the field of Computer Vision, Machine Learning and Embedded Systems.
Check the scientific publications
Stanisław Raczyński, PhD Distinguished professional with an impressive track record of 17 years in ML/AI and audio DSP research, coupled with 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.
Check the scientific publications
Karol Duzinkiewicz Senior Computer Vision Researcher at Seasoned engineer with many years of experience in international tech companies. Currently holds a team leader role in gaze estimation projects developed in the company. 
Check the scientifc publicatios
Michał Gorgoń Senior Embedded System Engineer at He graduated from the Electrical Technical School at the Zespół Szkół Łączności, specializing in Teleinformatics, and then pursued studies at the Electrical Department of the Wroclaw University of Technology, obtaining a Master's degree in Automation and Robotics.
Jan Glinko Machine Learning Researcher at He 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.
Michał Ostyk Computer Vision Engineer at He has experience in computer vision 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.
Artur Skrzynecki Machine Learning Researcher at He 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 networks training and evaluation. Apart from that, he also develops towards web development topics.
Cezary Polak Machine Learning Researcher at He 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 also in generating synthetic data as photos and texts.
Michał Affek Embedded Machine Learning Researcher at He is 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.

Make the first step towards smart manufacturing with us

Gain a competitive advantage with autonomous systems and other solutions powered by AI, machine learning, deep learning, AI, and more. We’ll happily combine digital technologies with the physical world to transform your industry. Contact us, and let’s get started.
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Check out some more of our Case Studies