According to the UK Government case study, UK Defence Innovation funding through the Security Open Call allowed Vizgard to develop artificial intelligence that automates some of the most demanding parts of drone operations, including piloting support and the tracking of hostile drones. The article places the company within a wider public policy problem: UK authorities want to use drones to improve safety and security, while also managing the risks created when the same technology is used by adversaries. That dual-use context matters. Busier airspace increases the value of rapid detection, reliable tracking and quicker operator decision-making, particularly where police and defence users are handling several live video feeds at once. In that sense, Vizgard's progress is not only a company growth story but also an example of how targeted public funding is being used to convert security technology into operational capability.
Vizgard is a London-based SME founded by former Royal Navy submariner Alex Kehoe. Over roughly five years, the firm has focused on software for operational drone challenges rather than new airframes, with the stated aim of making existing surveillance and drone systems more usable in live settings. With funding from UK Defence Innovation and technical input from the Defence Science and Technology Laboratory, known as Dstl, the company set out to support police drone operations beyond visual line of sight. Its platform, FortifAI, is described in the case study as a real-time visual AI system that can sit on top of existing camera infrastructure across land, air and maritime settings, automating detection, tracking and threat identification across multiple visual streams.
The most significant step came during the Security Open Call project. The government case study says Vizgard had to redesign the software so that it could move from handling a single camera stream for one drone to processing several streams at the same time. That re-engineering produced FortifAI 2.0, a production-grade system that the company says can handle dozens of concurrent streams and now process more than 100. For procurement policy, that point is important: early-stage government contracts can absorb technical risk at the point where a promising SME still lacks the commercial base to fund major platform redevelopment on its own.
According to the case study, UK Defence Innovation awarded Vizgard its first commercial contract in 2021. That project involved sea trials on the Royal Navy's uncrewed test submarine and gave the firm an early opportunity to prove that its software could operate outside a laboratory setting. The reported use case combined radio tracks with visual AI to identify vessels masking their identity and to automate the tracking of fast-moving craft. That sort of first deployment matters in defence markets, where reference projects, user confidence and evidence from trials often determine whether a smaller supplier can move into conversations with prime contractors and specialist security buyers.
The technology has since moved from funded prototype towards operational use, with UK policing exploring applications in both drone and counter-drone activity. The case study says the system can track friendly aircraft while also scanning the sky continuously for other aerial objects, giving teams a second layer of observation that does not depend on one person watching the right screen at the right moment. The practical issue is operator workload. Security and policing teams often manage dense visual data in real time, and missed detections can arise simply because attention is divided across too many feeds. Vizgard's offer, as presented by the government article, is therefore less about replacing personnel and more about directing human attention to the events that need a decision.
The commercial effect has been material. The UK Government article says Vizgard had around five employees when it first engaged with UK Defence Innovation and has since grown to about 20. In early 2025, the company also secured £1.5 million in venture capital, which it is using to develop UnifAI, a machine learning operations platform designed to shorten AI model improvement cycles from weeks to days, including for users without deep technical expertise. The business is also moving into export and alliance channels. The case study says Vizgard is supplying technology through a US Defense Innovation Unit contract for field testing with the US Marines. In policy terms, that sequence is notable because it suggests public R&D funding helped establish enough technical credibility to attract both private investment and overseas demand.
The next phase remains closely tied to public sector demand. Vizgard has been selected for NATO DIANA's 2026 cohort for autonomous and unmanned technologies, with the government case study noting that it was one of 150 companies chosen from more than 3,500 applicants. The same article says UK Defence Innovation support has also led to further work with Dstl on edge-based AI that helps quadcopter drones identify unexploded mines during automated intelligence, surveillance and reconnaissance missions. Taken together, the case illustrates a familiar lesson in defence-industrial policy. When funding is fully backed, technically supervised and tied to a defined operational problem, smaller suppliers have a clearer route from prototype to deployable capability. For officials tracking innovation spending, policing technology and dual-use procurement, Vizgard offers a concrete example of that model in practice.