{"id":29464,"date":"2026-05-12T14:08:36","date_gmt":"2026-05-12T11:08:36","guid":{"rendered":"https:\/\/thedefender.media\/?p=29464"},"modified":"2026-05-18T15:30:26","modified_gmt":"2026-05-18T12:30:26","slug":"clarity-feature","status":"publish","type":"post","link":"https:\/\/thedefender.media\/en\/2026\/05\/clarity-feature\/","title":{"rendered":"How AI helps analyse drone reconnaissance data: an interview with Clarity"},"content":{"rendered":"\n

In September 2025, Mykhailo Fedorov, who at the time headed Ukraine\u2019s Ministry of Digital Transformation, published a post about Clarity<\/a> \u2014 a Ukrainian software solution for analysing photo and video data. By then, the project had already secured several wins at hackathons and other events, including the international European Defense Tech Hackathon.<\/strong><\/p>\n\n\n\n

The company\u2019s hardware and software solutions help Ukrainian troops analyse reconnaissance data collected by drones faster and more accurately. Artificial intelligence identifies clues in photos and videos that humans \u2014 especially less experienced operators \u2014 may overlook. Thanks to Clarity, mission processing time is reduced from several hours to roughly 20 minutes.<\/strong><\/p>\n\n\n\n

The project is currently expanding its client base and further developing the product. As part of the Rewiring European Defense Tech<\/a> special project supported by European Defense Tech Hub, Defender Media spoke with Clarity founders Alex and Flame.<\/strong><\/p>\n\n\n\n

How was the Clarity project born?<\/h4>\n\n\n\n

Flame:<\/strong> Let me start with some context. My path in the military began in 2015, when I joined the Armed Forces of Ukraine as a volunteer with the rank of junior lieutenant. I served in the rear at one of the training grounds as an artillery and artillery reconnaissance instructor. About a year later, thanks to my background in IT, I was involved in developing the Kropyva tactical command and control system \u2014 I worked on programming the medical evacuation demo module.<\/p>\n\n\n\n

After demobilisation, I returned to civilian life. Later, through the professional community, I met a colleague who became a friend, and in 2019 we founded a cybersecurity company together. We raised a small amount of investment abroad, went through a startup accelerator there, and searched for product-market fit. In 2022, we received grants from two European innovation support programmes under Horizon 2020. Everything was going well until the full-scale invasion began.<\/p>\n\n\n\n

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Teaching mobilised soldiers artillery combat operations in the rear<\/figcaption><\/figure>\n\n\n\n

How did the start of the full-scale war change your work?<\/h4>\n\n\n\n

Flame:<\/strong> I decided to return to the Armed Forces. At first, I returned to the training ground, and later I was promoted to instructor at one of the higher military educational institutions.<\/p>\n\n\n\n

The turning point came during a deployment to one of the higher-level headquarters. I witnessed a situation where a UAV<\/a> operator flew past an important target without noticing it. Standing nearby was an intelligence officer who spotted the object in the video feed. The operator then returned for additional reconnaissance, the target was struck, and the officer received an award.<\/p>\n\n\n\n

It turned out to be a difficult but critically important target \u2014 a truck that was part of the high-value Zhitel electronic warfare complex. The only thing distinguishing it from an ordinary truck was an antenna positioned 50 metres away.<\/p>\n\n\n\n

That\u2019s when the idea emerged \u2014 something that was obvious in hindsight: a machine can notice such details far more effectively than a human if it is trained correctly.<\/p>\n\n\n\n

How did you move from the idea to implementation? Did you receive support from the military command?<\/h4>\n\n\n\n

Flame: We discussed it with civilian colleagues, and Lex built the first demo. We showed it to the commander of the missile forces and artillery. He liked the idea and said: \u201cCool thing, go build it.\u201d<\/p>\n\n\n

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\u201cThe sun of Ukraine rises over Donbas.\u201d Combat operations were conducted from this and similar shelters<\/figcaption><\/figure>\n<\/div>\n\n\n

It was impossible to work on such a project in my unit because quality training of cadets requires a huge amount of time, preparation and effort from instructors. It was demotivating, but not for long. I realised this product could have a much bigger impact on the course of the war than my own service. In classes, I could train a limited number of people, but technology can scale across the entire frontline, improving military efficiency and saving lives.<\/p>\n\n\n\n

So we started development in our free time, investing our own effort and money. In autumn 2023, I was deployed to Sloviansk. Paradoxically, despite combat and organisational duties, I had more free time there than in the rear. During those hours of rest, Lex, another colleague and I created the first working version. We distributed it among a small group of interested users, gathered feedback and started improving the product. We\u2019ve been working like this for more than two years now.<\/p>\n\n\n\n

What key milestones has the project gone through over those two years?<\/h4>\n\n\n\n

Flame:<\/strong> I\u2019ll explain chronologically. We started in April 2023 with the first demo. Back then, we were collecting datasets from open sources and trying to process them based on the practical experience of users. By August 2023, we convinced the first group of military personnel to try the product and received our first real feedback.<\/p>\n\n\n\n

An important milestone came in October 2023 when we completely redesigned the user interface. Previously, it was just a primitive form: specify a folder, press \u201cRecognise\u201d, get the result. The new version allowed users to browse photos, edit results and work with them much more conveniently.<\/p>\n\n\n\n

At what point did the volunteer project begin transforming into a proper structure?<\/h4>\n\n\n\n

Flame:<\/strong> The turning point came in January 2024. We hired our first employees \u2014 annotators. These are specialists who label objects in photos and videos, essentially preparing \u201cfood\u201d for AI algorithms.<\/p>\n\n\n\n

We realised that relying purely on volunteer work was ineffective in our case. Volunteers are difficult to manage and it\u2019s hard to maintain a stable work pace. So we moved to hiring staff. I organised a closed fundraiser among friends, we raised around $1,000, which was enough to pay annotators for the first month. After that, the project was funded entirely by the founders\u2019 own money.<\/p>\n\n\n\n

How did scaling and deployment in the military happen?<\/h4>\n\n\n\n

Flame: <\/strong>From early 2024 until September, we continuously released beta versions for units that agreed to work with us. It was a constant cycle: release \u2014 feedback \u2014 dataset expansion \u2014 improved recognition quality.<\/p>\n\n\n\n

In September 2024, we reached our first hundred users. Then in December, we won the Happy New Fear Hackathon<\/a> organised by Brave1, taking first place in the ready-made products category for deep reconnaissance.<\/p>\n\n\n\n

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Award ceremony for the winners of the Happy New Fear Hackathon<\/figcaption><\/figure>\n\n\n\n

I\u2019d also like to highlight an important personal milestone. In October 2024, I was transferred to a senior role at a research institution subordinated to Ukraine\u2019s Ministry of Defence<\/a>. My command now fully supports this project, enabling me to provide scientific and technical support within my service.<\/p>\n\n\n\n

When did the project transition from volunteer development to a commercial product?<\/h4>\n\n\n\n

Flame:<\/strong> The first sales happened at the beginning of 2025. It was a contract with what was then still a small Ukrainian drone manufacturer, but it confirmed the viability of the business model.<\/p>\n\n\n\n

After that, events accelerated quickly. In February 2025, we won the Trailblazers category at the Defense Tech Innovation Forum in Kyiv. In spring, we won a hackathon organised by the European Defense Tech Hub<\/a> together with the 3rd Assault Brigade<\/a>. Around the same time, we agreed to test a new feature \u2014 object recognition in video, not just photos.<\/p>\n\n\n\n

In May 2025, we signed a software licensing contract with one of Ukraine\u2019s leading drone manufacturers.<\/p>\n\n\n\n

In September, we won the Trailblazers category at Brave1 Defense Tech Valley<\/a>. More recently, Lex implemented a critically important feature \u2014 automatic detection of object coordinates in photos and linking them to maps.<\/p>\n\n\n\n

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Clarity team awards at Brave1 competitions<\/figcaption><\/figure>\n\n\n\n

All these steps allowed us to scale rapidly: by the end of summer 2025 we had reached 250 registered users, and now we already have more than 800.<\/p>\n\n\n\n

What does your roadmap look like for the near future? Where is the technology heading?<\/h4>\n\n\n\n

Flame:<\/strong> Right now, we are integrating our hardware module directly onboard UAVs. We plan to complete this stage with combat testing in the first half of 2026. This will allow data to be processed during flight rather than after landing. We are also integrating with the Delta<\/a> system.<\/p>\n\n\n\n

Lex:<\/strong> Our business goal is to reach break-even in the first half of 2026. Technologically, we are focused on improving video stream processing algorithms to achieve consistently high recognition quality. Our broader business goal is international expansion and exporting our solutions to NATO and EU countries.<\/p>\n\n\n\n

Regarding cooperation with drone manufacturers, can you name any companies you work with?<\/h4>\n\n\n\n

Flame:<\/strong> We have signed agreements that restrict disclosure. Without permission, we cannot name our partners. It\u2019s a matter of competitive advantage for them \u2014 using our software makes their products more effective, so they want to keep it confidential. However, two companies allowed us to mention them: Mara Drone and 2021 Solutions, which produces the BZIK reconnaissance drone.<\/p>\n\n\n\n

Could you explain what exact problem your product solves? Why did the existing reconnaissance process need automation?<\/h4>\n\n\n\n

Lex:<\/strong> The key point is that there are so-called \u201cphoto drones\u201d \u2014 UAVs that capture a series of high-resolution photos. They are cheap, and the image quality is extremely high \u2014 6K or 9K. Much better than video feeds or satellite imagery.<\/p>\n\n\n\n

But there\u2019s a problem: during one hour of operation, such an aircraft can take 1,000\u20132,000 photos. The files are huge, so they cannot be transmitted over radio channels in real time. After the flight, the crew has to manually analyse all this data. That can take up to eight hours. By then, the intelligence loses relevance, and people get tired and miss important details.<\/p>\n\n\n\n

Our main product is a desktop application that automates this process. The workflow is simple: the aircraft lands, the flash drive is inserted into a computer, the folder with photos is uploaded, and the system begins recognition.<\/p>\n\n\n\n

What exactly can the system identify in the images?<\/h4>\n\n\n\n

Lex:<\/strong> The algorithm identifies not only equipment. We detect indirect signs of enemy presence as well.<\/p>\n\n\n\n

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Example of aerial imagery processed with Clarity<\/figcaption><\/figure>\n\n\n\n

The process consists of three steps:<\/p>\n\n\n\n