We process complex data into Geo-Analytics

Based on the latest developments of the Big Data and AI technologies, we are able to process and correlate heterogeneous data and provide actionable insights.


We collect continuously through APIs relevant City Sustainability related data from a variety of open sources


Big Data Analytics open source technologies are exploited to analyse efficiently the collected data

Machine Learning

Machine Learning enables the correlation of heterogeneous factors into the desired aggregation that enables smart decision making

Deep Learning

Deep Learning Techniques are used to achieve sensor based Object Recognition technology to be used in Household and Smart City applications

Smart Navigation

Smart Navigation algorithms are developed in house enabling advanced routing suggestions for citizens and visitors promoting green mobility 

City Insights

 GeoAnalytics of collected environmental data  produce Actionable Insights for City Sustainability progress

EasyRide Project

At the core of the new technology developed, we will have an innovative model based on Machine Learning that will produce the attractiveness of the road segments for a particular time of day and type of mobility. The routing algorithm used will be developed by our group and will be a combination of the best graph theory algorithms (Dijkstra, Bellman-Ford) and Artificial Intelligence (A* Heuristic Search, etc.)

PCViD project:

End-to-end system of object detection and recognition that takes as input sparse point clouds collected by cheap and easy-to-deploy sensors.  RGB-based object detection models operate directly on RGB images, since they are dense in pixel space and encapsulate notions such as shape and texture. This is not the case for sensor point clouds. Therefore, a crucial part for the success of sensor-based object detection is the development of reliable shape reconstruction/recovery methods from point clouds.

AI4FS Project

An AI based Solution for Automated Forest Surveillance.

Country: Greece, Norway

Areas of Experimentation: Timeseries forecasting using both off-the-shelf and custom models, Geospatial models for location-based optimization.

CNROpt Project:

Charging Network Rollout Optimization is an AI project aiming to disrupt the way Electric Vehicle Charging Networks are being planned. Utilising timeseries data for charging sessions, demographic and seasonality data it will predict the optimal sites for building new EV charging stations

Optimizing EV Charging Station Rollout
AI4CS Project:

Our Solution is aiming to utilize the short-term forecasts for CS utilization/availability we already produce and combine it with several data sources existing at Interconnect SIF in a new app that will provide valuable insights for the end-user, the EV owner, suggesting to them where and when to do their next charging.


We process the raw data collected for the city seamlessly and provide real time analytics to the stakeholders.

Insightful Presentation

Flexible way of providing the isnights from desktop detailed maps to smart routing mobile apps.

Monitoring Progress

Monitoring the Evolution of the Climatic Performance of the CIty is essential to support decision and revision making on the city transformation plans

Enabling Smart and Sustainable Cities

The insights based on the Geo Analytics can help better planning of the various actions that will reduce Carbon Emissions of our Cities. The efficiency of each transformation action will be measured in real time and ant inefficiencies can be corrected immediately

Get Started with Local AI

Contact us for a detailed consultation about the available datasets and delivery options as well as mobile apps for content/solutions delivery.

We can deliver our Software by the SaaS model providing a hassle free operation paradigm.

+30 2721 028025
+30 6977 277564


2 Vassilisis Amalias str.
Kalamata 24100