In March 2025, Tech Mahindra officially launched its Altavec geospatial data and analytics platform. Altavec provides geospatial data management and operations across large-scale mapping initiatives, a priority area for asset-heavy industries.

From spatial data to actionable insights

In 2022, Tech Mahindra acquired a majority stake in Australian AI firm Geomatic.AI for AUD 6m. Geomatic.AI specializes in digital platforms, most notably geospatial data management, and has a strategic partnership with an Australian energy and utilities distributor. Using the existing IP and expertise and Tech Mahindra’s previous experience in analytics and network management in telecom, the company launched the Altavec platform in 2025.

Altavec provides the platform, data collection services, and analytics resources. It is cloud-based and has six different modules:

• AIMS Clearance for automated data classification and processing. It has an analytics engine specialized in physical asset maintenance analysis. AIMS Clearance offers a 3D summary overview of the network, facilitates desktop site studies, and provides tools to plan maintenance, manage operational risks (e.g., vegetation impacting powerlines or distance to the ground), set priorities, and understand asset relationships
• AIMS Capture for spatial data ingestion from in-air or on-the-ground image capture, wide area mapping, LiDAR remote sensing, IoT sensors, and panoramic imagery to create precise 3D models
• AIMS 3D, an interface to LiDAR data, visualizing via millimetre-accurate 3D models for field-based assets. It combines ML and spatial algorithms to render network assets and the surrounding environment with high precision. AIMS 3D creates 360-degree panoramic imagery, orthorectified imagery, and high-resolution asset images with LiDAR data and GIS information. AIMS 3D includes analytics and simulation tools to assess network performance, identify potential risks, and optimize operations. It also has a self-service administration module to manage data, visualisation, and user permissions to enable access and decision-making across functions in organizations without requiring advanced technical skills
• AIMS VAA is Altavec’s Virtual Asset Assessment solution, with a map layout with GIS layers, providing spatial context for the asset review. AIMS VAA gives access in real-time to asset condition data from various sources with self-serve image upload and automated ingestion APIs, and uses AI/ML for insights generation. AIMS VAA utilizes metadata to achieve accurate image-to-asset associations. It allows single sign-on for company-wide usage and has features for collaborative workflows, team member assignment, and data access control. Other capabilities include tailored defect forms with customized attributes and 2D map navigation. For developers, the Restful APIs enable smooth integration with defect management systems, GIS systems, and other applications
• AIMS Zero is a lone worker protection solution using a web-based application and a wearable watch or mobile device (both iOS and Android). AIMS Zero offers real-time alerts; for example, when a field worker approaches a known hazard, work site check-in & check-out, and panic and duress alarms activated by workers to escalate to safety contacts and emergency services. AIMS Zero also supports the identification, documentation, and mapping of hazards for organizations to create a repository of risks faced by field workers
• FMC (Field Mobile Computing) for field users to complete asset inspections and management via a mobile app and synchronize results. One of the use cases of FMC is meter reading.

Altavec has ~30 existing accounts, mainly in AN&Z and North America.

Demand for lower-cost and preventive asset management at scale

Clients expect geospatial analytics to speed the transition from the break-fix model to the preventive-maintenance model. They want to achieve safer and more efficient management of their field-based assets by allowing remote assessments, risk identification, and planning. The requirements are for automation of manual inspection processes, reduced need for field visits, and improved safety for remote workers through real-time alerts and accurate location tracking. They also look for capabilities that optimize resource allocation, lower OPEX, and enhance network reliability through accurate, timely, and actionable spatial insights.

While industries such as energy and utilities, rail, telecommunications, and government have invested over the years in third-party GIS (e.g., ESRI) and in-house mapping tools and analytics engines, they have rarely integrated these capabilities into a centralized and democratized geospatial analytics platform. Tech Mahindra Altavec identifies this gap as its big opportunity.

With increasing construction costs, additional worker safety regulations, and the rise of external threats, including environmental disruptions and bad actor activities, the demand for reliable and proactive issue detection at scale is expanding rapidly. A stark reminder has been the 2019-2020 Australian bushfires, which accelerated asset-heavy organizations on the continent to invest in proactive monitoring and risk detection.

An example is an Australian energy company providing energy in Western Australia. The company has network assets spread across a vast area, including remote and arid terrains, and was facing challenges in network maintenance and repair works. Each network area was operating from separate GIS with inaccuracies on asset locations and a disjointed view of operational and maintenance processes. Tech Mahindra Altavec has been working with the client since 2018, starting with field capture programs using LiDAR. In the following years, Altavec leveraged partners for aerial capture using helicopters, with equipment fitted on them and manual image analysis covering 5.8k network km, ~57k spans, and ~58k poles. The ~300 aircraft survey hours and ~100 mobile survey hours captured around 15T of raw data.

The Altavec team created a centralized repository of the captured data, rendered the full network in 2D and 3D, matched to the utility assets, and tagged with defects or vegetation encroachments. Using AI and ML techniques, it scanned thousands of images in minutes and guided the client to areas at risk. The automation reduced the cost of span inspection by ~50% compared to manual methods. The client accurately identified and quantified non-compliant asset conditions and prioritized asset maintenance and repairs, while the targeted preventive maintenance increased the life of assets and network reliability. The access to geospatial information assisted in planning and prioritizing before crews begin fieldwork, and

reduced site assessment visits and field travel time. The situational awareness of the network improved preparedness during emergency events and natural disasters.

Comprehensive AI-powered geospatial insights platform

Altavec stands for algorithm, altitude, and vector to indicate the importance of data and AI integration. Tech Mahindra sees a significant benefit of its Altavec BPaaS offering as its flexible modular architecture that facilitates automation, scalability, and integration with clients’ existing stack.

With its strong ITS and BPS presence in the telecom sector, Tech Mahindra is now targeting its Altavec capabilities for sector clients in the U.K., U.S., and AN&Z. In addition to data ingestion and AI-powered image analytics, Tech Mahindra plans to integrate its automation stack for automated creation of work orders in telecoms’ backend systems; for example, truck dispatch for cell tower maintenance. An interesting development is the proliferation of drone-in-a-box solutions for mapping and maintenance automation of towers or electric substations.

Other promising opportunities are in the public and transportation sectors for areas such as infrastructure planning and vegetation management. Existing deployments of Altavec include the realization of a digital twin project for Sydney Trains; 3D imagery and LiDAR data collection for the Bay Area Rapid Transit in San Francisco; and capturing and mapping signage and trees for the City of Greater Geelong (Victoria, Australia) to plan future vegetation planting efforts.

Tech Mahindra is enhancing Altavec by focusing on integrating additional third-party data, such as morphing satellite imagery with collected sensor data and information available from hyperscalers such as Google. One possible benefit is the lower cost of the acquisition. It plans to utilize its crowdsourced work platform Populii for manual geospatial data conversion and conflation. The need for manual data cleaning and AI training remains in some data types and use cases, and Tech Mahindra expects to benefit from the scale of its gig resources and its domain expertise.

Finally, AI integration in Altavec is a major investment track for Tech Mahindra. The company plans AI infusion into data capture, data processing (e.g., computer vision), and analysis. A target opportunity is AI-powered predictive maintenance; for example, in vegetation growth.