A public-services operator needed to place service branches for maximum accessibility under real-world constraints. We combined geospatial machine learning, optimization and digital-twin simulation to validate the plan before anything moved.
Branch placement decisions were made with limited data and no way to test outcomes in advance — risking expensive moves that might not improve access or reduce waiting times.
Modeling over population density, transport accessibility and demand to understand where service is needed most.
Optimization that searches placement options against multiple objectives and constraints.
A simulation of the network to validate the proposed plan and predict outcomes before committing.
Clear, explainable outputs so decision-makers could trust and act on the recommendations.
Figures reflect outcomes measured on this engagement. Client withheld under NDA.
Data modeling, optimization and simulation worked together: ML found the patterns, optimization searched the options, and the digital twin let stakeholders validate the plan before a single office moved.
We scope a clear plan with milestones and architecture options — and right-sized GPU hardware if AI workloads are involved.