May 2019
We started Machine Can See to build production-grade computer vision for physical environments—systems that work reliably outside of controlled lab conditions.
July 2020
Machine Can See secured an equity-free pre-seed investment of 115k EUR from the Republic of Serbia’s Innovation Fund.
September 2020
We hired our first employees and started building in-house R&D and engineering capacity.
April 2021
A proposed approach for outdoor parking occupancy using computer vision and smart edge devices, combining new data streams to build a 3D understanding of the environment and identify available spaces.
May 2021
Selected as one of 27 startups (out of 124 applicants) to participate in the program, supporting our mission to bring Visual AI to infrastructure and smart-city use cases.
Learn more →
June 2021
Machine Can See’s patent aims to improve object prediction using camera imagery, focusing on objects such as vehicles, buildings, and roads. The system generates a vector-based representation of each object through a neural network, stores it in a database, and uses it to create a 3D simulation of the observed environment.
By incorporating physics-based models, the system increases accuracy and realism, enabling more precise predictions of real-world scenarios.
Find out more about our patent →
March 2022
We built and tested an outdoor-ready prototype, validating key performance and deployment assumptions.
May 2022
An approach to estimating 3D object orientation from a single image using geometry-based methods.
Find out more about our whitepaper →
June 2022
Landing our first major paying client in the US validated real market demand for our technology and deployments.
2026
We build and deploy production-grade visual AI—starting with GateGuardX and expanding into parking and traffic analytics.
We’re a product-focused engineering team building applied computer vision—from edge deployments to cloud dashboards.
We collaborate with experienced researchers and industry experts in Visual AI and applied machine learning.
Professor at the University of Novi Sad. PhD in Computer Engineering (Florida Atlantic University).
Works in Visual AI and neural networks. NVIDIA Deep Learning Institute University Ambassador.