\n" }];

Would you like to share route planning with your customers in your app? Use the On-Street Parking API.


  1. pl

+48 606 153 759


Laboratory of spatial analysis

To use the On-Street Parking API, please contact us.

Our partners, just like us, value solutions that support sustainable development. Meet them.

Scientific and business partners

We combine the possibilities of artificial intelligence with the analysis of spatial data to create modern solutions that support sustainable development.

Let's create something amazing. Let's talk about projects


About us

We create products tailored to the needs.

We focus on ultra-modern, user- and environmental projects. We want our solutions to make your life easier.

Just like that.

 We devote 100% of our time, energy, and dedication to the projects we implement

- Assists users in finding parking options near their destination,

- Allows to plan trip up to 24 hours in advance

- Gives opportunity to choose parking spaces based on their availability upon arrival

- It allows to navigate to the exact parking spot from the current location

- We provide information about the location of the parking lot,

- We provide information about paid parking zone, such as opening hours and prices

- We provide dynamic real-time parking information for a specific location and 24-hour forecasts

ParkSpace ECO solves the problem of parking in 55 Polish cities.

The project is consistent with the idea of sustainable development and environmentally friendly with ParkSpace ECO.

City well parked

Inspired by AI


ParkSpace ECO is a modern technology platform that uses artificial intelligence (AI) and spatial data analysis to provide the user with information about the probability of parking in the destination with an accuracy of up to 95%.

Innovative AI-based solution.

A team of the best specialists has developed a system that will revolutionize the parking industry. The innovative software infrastructure, based on the cloud, is an alternative to traditional solutions based on a network of sensors. It is based on the use of machine learning algorithms, the analysis of factors such as weather, time of year and day, traffic volume and comparing them with historical data from parking meters. All this to estimate the availability of parking spaces in unguarded paid parking zones.

The overwhelming advantage of our solution is the provision of information about parking options at the estimated time of reaching the destination, not when it is searched.

Phenomenon on a global scale