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.
Keep Reading
As workplace knowledge spreads across chats, AI firms are building systems that can structure, retrieve and preserve it over time.
Updated
May 11, 2026 5:24 PM
A messaging app on a phone. PHOTO: ADOBE STOCK
Votee AI, an enterprise AI company headquartered in Hong Kong, has partnered with its Toronto-based research lab Beever AI to launch Beever Atlas. The new platform is designed to turn workplace chats into searchable knowledge that AI systems can retrieve and understand.
The release focuses on a growing issue inside organisations. Much of today’s workplace knowledge now exists inside chat platforms such as Slack, Microsoft Teams, Discord and Telegram. Important discussions, project decisions and technical information often disappear into long message histories that are difficult to search later.
Beever AI developed the platform to organise those conversations into a structured system for AI assistants. The software connects with Telegram, Discord, Mattermost, Microsoft Teams and Slack, then converts conversations into linked records of people, projects, files and decisions.
The collaboration combines Votee AI’s enterprise infrastructure work with Beever AI’s research around AI memory systems. The companies are releasing two versions of the product. The open-source edition is aimed at individual developers, researchers and creators. The enterprise edition is designed for banks, government agencies and larger organisations with stricter security requirements.
The release also reflects a broader shift happening across the AI industry. Companies are increasingly looking at how AI systems store and retrieve long-term knowledge, rather than relying solely on large context windows or search-based retrieval.
Earlier this year, OpenAI founding member and former director of AI at Tesla Andrej Karpathy discussed the growing need for what he described as “LLM Knowledge Bases.” He argued that AI systems need structured and evolving memory rather than depending only on context windows and vector search.
Beever Atlas approaches that problem through workplace communication. Instead of focusing mainly on uploaded files, the system is designed around conversations that happen daily across team chat platforms. It can also process images, PDFs, voice notes and video files within the same searchable system.
The companies say the software is designed to work directly with AI assistants and coding tools such as Cursor, AWS Kiro and Qwen Code. Integrations for OpenClaw and Hermes Agent are expected later in 2026.
Pak-Sun Ting, Co-Founder and CEO of Votee AI said: "Hong Kong has always been known for property and finance. Beever Atlas is proof that world-class AI infrastructure can emerge from an HK-headquartered company and be shared openly with the world. Every growing organization faces the same silent liability: conversational knowledge loss. Beever Atlas turns this perishable resource into a compounding organizational asset."
A large part of the enterprise version focuses on privacy and access control. The system mirrors permissions from Slack and Microsoft Teams so users can only retrieve information they are already authorised to access. Permission updates are reflected automatically when access changes inside company systems.
The enterprise edition also includes audit logs, encryption controls and data retention settings for organisations handling sensitive internal data. Companies can run the software entirely inside their own infrastructure using Docker and connect it to their preferred AI models through LiteLLM.
The companies argue that organising information is more useful than simply storing chat archives. Jacky Chan Co-Founder and CTO of Votee AI said: "The key technical decision was to treat agent memory as a knowledge engineering problem, not a retrieval problem. Structure beats similarity — a typed graph of who works on what is more useful to an AI than vector search over a Slack archive."
The software also includes protections against prompt injection attacks and systems designed to reduce hallucinated responses. According to the companies, the AI is designed to return “I don't know” with citations when confidence is low instead of generating unsupported answers.
As workplace communication becomes increasingly fragmented across chat platforms, companies are beginning to treat internal conversations as information that AI systems can organise, retrieve and build on. Beever Atlas reflects a broader push to turn everyday workplace communication into long-term organisational memory.