Scaling & Growth

ZenaTech Expands Drone Startup Strategy Into Canada’s Oil and Gas Industry

As industrial drone adoption grows, startups are finding bigger opportunities in infrastructure, inspections and field operations.

Updated

May 25, 2026 3:21 PM

An oil pump on a field. PHOTO: UNSPLASH

As drone adoption grows across industrial sectors, more startups are moving beyond hardware sales and into service-based business models. Instead of simply selling drones, companies are increasingly trying to build recurring revenue through inspection, mapping and infrastructure-monitoring services. That shift is shaping ZenaTech’s latest expansion strategy.

ZenaTech is a Vancouver-based startup that develops AI drone and Drone as a Service (DaaS) technologies. The company has signed an offer to acquire an Alberta-based land surveying and geomatics business operating across Western Canada. If completed, the deal would mark ZenaTech’s first land surveying acquisition in Canada and its first major push into the oil and gas sector.

The move gives the startup something more valuable than just another acquisition target. It provides direct access to an industry where drones are already becoming part of everyday operations.

The Alberta surveying company works with oil and gas producers across Alberta, Eastern British Columbia and Saskatchewan. Its services include land surveying, geomatics, mapping and environmental support for infrastructure and energy development projects.

According to ZenaTech, drones are already used in roughly 80 percent of the target company’s existing projects. That matters because it reduces the operational gap between traditional surveying work and AI-powered automation.

Rather than introducing drones into a completely manual workflow, ZenaTech is entering a business where drone-based data collection is already established. The startup says it plans to build on that foundation by integrating more AI-powered capabilities across surveying, mapping, inspections and infrastructure monitoring.

Shaun Passley, Ph.D., CEO of ZenaTech, said: "This proposed acquisition represents an important strategic expansion of our Drone as a Service business into Canada’s oil and gas sector, one of the most significant energy markets in North America. This company brings an established commercial customer base, strong regional expertise, and extensive experience supporting surveying and geomatics projects including for some large producers. We believe there is a significant opportunity to further enhance these services through AI-powered drone technology for surveying, mapping, inspections, and infrastructure monitoring applications, enabling us to establish a core expertise that we can bring to this fast-growing global industry."

The timing is also significant. ZenaTech pointed to estimates showing the global oil and gas drone inspection services market is currently valued at around US$ 2.3 billion and projected to grow at a compound annual growth rate of roughly 28.5 percent.

Much of that growth is being driven by energy companies looking for faster ways to inspect infrastructure, monitor remote sites and reduce manual field operations.

ZenaTech’s broader strategy centers around building a global DaaS network through acquisitions. Instead of creating local operations from scratch, the startup is acquiring existing service businesses with established customers and then layering drone automation and AI systems into those operations.

The company says its DaaS platform offers businesses and government clients subscription-based or on-demand drone services across areas such as inspections, surveying, maintenance, inventory management and precision agriculture.

The larger opportunity for startups in this space may not be drone manufacturing alone. Increasingly, the focus is shifting toward startups that can build scalable drone service networks and integrate them into industries that already rely on large-scale field operations. Oil and gas appear to be one of the next major targets for that expansion.

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Health & Biotech

Healthcare Innovation: A New Simulator for Faster Endometriosis Diagnosis

Endometriosis often takes years to diagnose. This ultrasound simulation innovation could help change that

Updated

April 13, 2026 3:18 PM

A group of women facing backwards. PHOTO: UNSPLASH

Endometriosis affects roughly one in ten women worldwide, yet diagnosing the condition often takes years. In many cases, patients experience symptoms for nearly a decade before receiving a confirmed diagnosis. One reason is that detecting endometriosis through ultrasound requires specialized training and clinicians do not always encounter enough real cases to build that expertise.

To address this gap, medical simulation company Surgical Science has introduced a new ultrasound training module designed specifically for identifying endometriosis. The system allows clinicians to practice scanning techniques in a virtual environment, helping them recognize signs of the disease without relying solely on real-patient cases.

A key feature of the simulator is training on the “sliding sign,” an ultrasound indicator used to detect deep endometriosis. Because the condition can appear differently from patient to patient, mastering this assessment in real clinical settings can be difficult. The simulator allows clinicians to repeat the process across multiple scenarios, improving their ability to identify the condition during routine examinations.

The module also incorporates the International Deep Endometriosis Analysis (IDEA) protocol, which provides a structured method for performing a complete pelvic ultrasound assessment. Additional training cases, region-based scenarios and certification options are included to support standardized learning.

Early training results suggest strong improvements in clinician confidence, including higher skill levels in transvaginal ultrasound and better recognition of deep endometriosis. By expanding access to structured ultrasound training, simulation tools like this could help reduce diagnostic delays and improve care for millions of women living with the condition.