How It Works

Benefits

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FlowSense AI

Reimagining Traffic Analysis with Graph Neural Network

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Import your data.

Upload traffic counts, travel times, and roadway geometry for instant setup.




AI calibration engine.

Automatically analyzes patterns and calibrates model parameters using GNNs and real-time data.




Validated outputs.

Accurate, ready-to-use models for scenario testing and better planning results.




What we do?

FlowSense is building the next generation of traffic analysis and infrastructure scenario evaluation — a Graph Neural Network (GNN)–based foundational model that learns from real-world data to understand and predict traffic behavior.

Traditional simulation tools require months of manual calibration and often fail to capture the complexity of how transportation networks operate. FlowSense changes that.

Our mission is to empower agencies and engineers with an intelligent platform that continuously learns from data — reducing modeling time from months to minutes, and enabling smarter, faster infrastructure decisions.

Our Current Focus

We are currently developing our prototype and training our first foundational model using real-world roadway data.

We’re collaborating with transportation agencies and domain experts to identify underutilized datasets and build the foundation for a scalable, adaptive traffic modeling engine.
We’re passionate about connecting planners and engineers with seamless tech to transform transportation modeling.


Our Team


Our founding team combines deep expertise in transportation engineering, data science, and machine learning. Together, we’re uniting domain knowledge with cutting-edge AI to revolutionize how mobility systems are designed and managed.

Najmeh Jami, PE
CEO and Founder - Transportation engineer with years of experience in traffic modeling, simulation, and corridor studies, leading projects for state and city agencies. Passionate about leveraging AI for smarter infrastructure decisions.

Roy Forestano, PhD
Founding Research Scientist - Machine learning researcher with deep expertise in graph-structured data, geometric deep learning, and sequence modeling. Interested in developing scalable, accurate, and explainable computational models which integrate well-tested theory with real-world mobility networks.

Pieter Moens, PhD
Founding Machine Learning Engineer - Machine learning engineer with end-to-end expertise in graph data science, scalable architectures and MLOps to bridge research, engineering, and deployment for reliable real-world mobility networks.

Jason Morris
Advisor - Business strategist, notable for building scalable tech platforms and leading cross-functional teams. Expert in product vision, operations, and forming impactful partnerships.