Geospatial firm Genesys has appointed Dhiman Basu Ray, a longtime executive from Tech Mahindra, as its new chief technology officer. This hire signals a strategic expansion into AI-powered spatial intelligence, moving beyond traditional mapping and 3D digital twins toward a unified platform that merges real-time geospatial data with advanced machine learning. Basu Ray brings over 26 years of expertise in artificial intelligence, robotics, cloud infrastructure, and digital engineering.
His primary responsibility will be architecting an AI-native framework that transforms Genesys’ high-resolution mapping, surveying, and spatial datasets into an intelligent, machine-interpretable layer. The company states this will unlock applications in smart urban development, autonomous vehicle navigation, and large-scale infrastructure optimization. Sajid Malik, chairman and managing director of Genesys, said the company is moving from mapping the world to making the physical world computable.
The approach mirrors the growing field of Physical AI, where AI systems operate directly on real-world environments through continuous, high-fidelity spatial data. Unlike conventional AI—limited to digital abstractions, this model requires precise 3D modeling and real-time updates to function effectively. Genesys currently offers specialized services in terrestrial and aerial surveying, high-definition mapping, and city-scale digital twins. Basu Ray’s experience in large-scale platform design will help the company develop systems capable of processing, validating, and deriving insights from vast volumes of spatial information.
The transition aligns with industry shifts where geospatial providers are embedding AI to convert static maps into dynamic decision tools for sectors like transportation, energy grids, and public utilities. The company’s roadmap centers on fusing its existing geospatial assets with AI-driven analytics to establish a live spatial intelligence network. Basu Ray said the next opportunity for Genesys lies in combining AI, digital engineering, and platform thinking to build a scalable spatial intelligence layer.
Basu Ray’s arrival also shows the company’s commitment to infrastructure that supports autonomous systems. His prior work in cloud-native architectures suggests a focus on scalable, cloud-hosted solutions for spatial data processing. The integration of AI into geospatial workflows is expected to reduce manual interpretation errors and accelerate project timelines in fields where precision is critical.
By embedding machine learning directly into spatial data pipelines, companies can shift from reactive mapping to proactive spatial intelligence, identifying patterns, forecasting infrastructure needs, and automating decision-making in complex environments.
