Power your drone with adaptive flight automation
Joined Geovation Accelerator Programme
At Hammer, we’re building an adaptive autopilot for drones. We believe drones are going to have a profound impact on the world. They will completely transform logistics, remote sensing, asset management and more. We also believe software/AI platforms are going to play a key role in it. With that in mind, our mission is:
“To enhance human potential by investing in Unmanned Aerial Vehicles (UAVs)”
Commercial drones are now being used for a number of different use-cases: mapping farms, inspecting buildings and SAR operations. However, most automation software available today is specialised towards 1 specific type of drone and use-case. This means that drone operators are forced to use many different types of software to get their job done, which becomes incredibly expensive and unreliable over a period of time.
Hammer is the world’s first *adaptive* flight automation software for unmanned aerial vehicles. It is highly versatile in that it supports many different types of flight automation, and yet is extremely simple to use. Using artificial intelligence and modular software architecture, the software morphs itself based on the task at hand. Think of it like Microsoft Windows or Apple’s OSX for Drones.
The commercial drone industry is currently worth $13 billion in value and is expected to grow to $45 billion by 2025 (CAGR 44%). A lot of this growth is attributed to enterprises rapidly setting up in-house drone teams and integrating drones into their existing workflows.
We launched Hammer in 2019 and since then we have acquired and retained 1000s of drone operators on our platform, and also signed enterprise contracts with in-house drone teams. More importantly, we’re growing at a rate of 12% monthly (organically) without spending a single dollar in marketing. We have also secured strategic partnerships with other players in the drone ecosystem – data processors, drone operators and training providers to spur our next stage of growth.