Our Story
Built in the Field.
Designed for Scale.
From Field Experience
Where the Gap Became Clear
Aerial Conservation Monitoring began through direct field deployment experience in Kenya, working alongside ranger teams and conservation managers. On the ground, it became clear that landscape scale wildlife census remained expensive, episodic and heavily manual.
Helicopter surveys were cost prohibitive and infrequent, while ground based monitoring produced fragmented datasets. Protected areas lacked reliable, repeatable landscape intelligence at the scale required for modern conservation. The problem was not effort. It was infrastructure.
From Census to Infrastructure
Building Structured Aerial Intelligence
ACM was created to replace episodic surveys with structured aerial census deployments. By combining fixed wing drone transects with onboard AI inference and georeferenced outputs, the system delivers fast, repeatable wildlife intelligence across tens of thousands of hectares in days rather than weeks.
The platform supports single species verification, multi species estate census and livestock enumeration, producing audit ready outputs compatible with existing GIS systems. What began as a proof of concept deployment at Borana Conservancy has evolved into a scalable model for recurring monitoring cadence and long term landscape visibility.
Fixed wing drone census also reduces direct CO₂ emissions by over 90 percent compared to traditional helicopter based surveys, making large scale monitoring structurally lower carbon as well as lower cost.
How We Build
A Structured, Field-First Approach
Field-Validated Design
ACM is built from operational experience, not theory. Every deployment model is tested in real conservation landscapes, working alongside rangers and aerial partners to ensure practicality, regulatory compliance, and operational feasibility. Technology is adapted to field constraints: heat, endurance, airspace regulation, and data workflows, before it is scaled.
Infrastructure, Not Projects
Scaling Structured Aerial Census