Move goods smarter. Improve warehouses, delivery and supply chains with robotics, automation, AI or data.

Where do delays, repetitive tasks or costly decisions slow things down? Prototype an approach that makes a measurable difference.

Possible directions: warehouse workflows, delivery processes and movement of goods.

Help people and robots work better together. Build safer workplaces, assistive technologies and clearer interactions.

Start with the person using the system. Demonstrate how your solution makes an interaction safer, easier or more effective.

Possible directions: assistance, collaboration and human - robot interaction.

Make urban systems more useful. Explore mobility, infrastructure, public services and resource management.

Choose a specific urban problem and its users. Show how AI, robotics or connected technologies could support a practical solution.

Possible directions: monitoring, mobility and better use of resources.

What useful job could a robot take on in a business or public space? Explore practical applications for the Booster K1 EDU robot using a simulation environment and expert guidance. Promising ideas could be developed further and showcased at AI Studio.

Many businesses are curious about AI but struggle to see where it could help in their daily work. Create an interactive demonstration that lets visitors try a practical AI application themselves, understand its value and limitations, and discover opportunities for their own organisations.

Fishing nets can be difficult to spot, creating risks for boaters and making monitoring harder for inspectors. Riga invites teams to explore digital solutions that make deployed fishing gear visible, connect waterway users and simplify reporting of suspicious or unregistered gear.

Snow, ice and blocked sidewalks can make everyday journeys unsafe, while municipal teams have thousands of locations to monitor. Help Riga explore how automated monitoring could detect and locate winter hazards earlier, giving maintenance services better information about where action is needed.

Repackaging incoming goods can be time-consuming and physically demanding for warehouse employees. Manual handling may involve safety risks, particularly when dealing with heavy, bulky, or awkwardly shaped items. Automating parts of the process could reduce physical strain, minimize the risk of workplace injuries, and improve overall process efficiency. The goal of this simulation is to understand how the repacking process can be optimized by simulating different repacking schemes and provide the most efficient approach, i.e. to minimize movements during repacking, to repack products to fit the warehouse’s storage bins. At the same time, a robot with sufficient lift capacity can replace humans who “flip” the product, and by doing so, increasing safety of warehouse staff.

After unloading of trucks or containers, goods must be visually inspected for quantity discrepancies and potential damage. However, pallets are sometimes positioned so closely together that employees cannot easily access all sides to perform a thorough and safe inspection. A robot can move between the material and visually inspect the goods, check the quantity, compare with the order. At the same time, the robot improves safety because no human has to squeeze between pallets and endanger their health and safety.

Manual stock checks in the warehouse are time-consuming and resource-intensive. Discrepancies between physical stock and SAP inventory data, incorrectly stored handling units (HUs), unrecorded free storage locations, and discontinued products remaining in the warehouse can lead to inventory inaccuracies and inefficient warehouse operations. In addition, product damage at individual storage locations may go unnoticed. A robot can independently move between the aisles and visually check the storage bins for above mentioned issues, compare the physical stocks with virtual stocks and report to warehouse supervisor with tailored proposal to address issues.

Latvijas Pasts maintains a nationwide network of public letterboxes, but the amount of mail inside each box becomes known only when it is opened for collection. Between visits, there is limited visibility into how quickly mail accumulates or how usage differs from one location to another.

This information gap makes it difficult to assess which letterboxes are used most, how collection needs change over time, and whether existing locations and collection routes reflect actual demand. Collection planning and decisions about letterbox placement therefore rely on an incomplete picture of how the network is used.