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Drones & Edge AI Systems
Edge Computing, Real-time Processing & Neural Network Optimization
AI does not always have to be in the cloud. Sometimes it has to be where things happen.
Sending data to the cloud, waiting for a response and acting accordingly works when latency does not matter. But when a drone needs to detect an anomaly in flight, a sensor needs to react to an event in milliseconds, or a system needs to operate without stable connectivity, the cloud is not an option — it is a bottleneck.
We design AI systems that run where the data originates: on edge hardware, embedded devices, onboard systems.
Problems we solve
Blocked knowledge, critical dependencies and inefficiencies holding back growth
Latency incompatible with the operational context
In real-time applications every millisecond counts. An architecture that depends on cloud round-trip introduces delays the use case cannot tolerate.
Absent or unreliable connectivity
Drones in flight, field sensors, remote industrial plants: in many operational contexts connectivity is intermittent or absent.
Models too heavy for available hardware
A model trained without constraints does not run on an edge device. Optimization is needed — quantization, pruning, distillation.
Out-of-control bandwidth and cloud costs
Continuously transmitting high-frequency video, audio or sensor data streams to the cloud is expensive.
Use Cases
Real projects, measurable results
01
Computer vision pipelines optimized to run directly on drone hardware, eliminating connectivity dep…
Drones — Onboard processing for in-flight analysis
Computer vision pipelines optimized to run directly on drone hardware, eliminating connectivity dependency for real-time footage analysis. Anomaly detection, object detection and classification happen onboard.
Zero connectivity dependency. Analysis happens where the flight happens.
02
Sound analysis pipeline deployed on edge hardware for continuous acoustic monitoring in urban envir…
Real-time audio processing on public infrastructure
Sound analysis pipeline deployed on edge hardware for continuous acoustic monitoring in urban environments. Sound event classification and anomaly detection happen locally, with transmission to the central system of only relevant events.
Only relevant events are transmitted. The rest is processed and discarded locally.
Technology Corner
The architecture and technologies that make the difference
Our projects don't rely on off-the-shelf solutions. Every system is designed on the right architecture for the specific problem — and the technology choice makes the difference between a tool that works in demo and one that holds up in production.
Neural network optimization
- ✓ Quantization (INT8, INT4)computational footprint reduction without significant degradation
- ✓ Knowledge distillationtransferring performance from large models to lightweight edge-deployable architectures
- ✓ Compilation and optimizationTensorRT, ONNX Runtime, OpenVINO for specific hardware targets
Edge deployment and real-time processing
- ✓ Optimization for edge targetsNVIDIA Jetson, ARM devices, microcontrollers, dedicated SoCs
- ✓ Stream processingfor handling continuous flows from sensors, microphones, cameras and GPS
- ✓ Hybrid edge-to-cloud pipelineslocal processing for time-critical decisions and selective cloud transmission
Interested in this service?
Contact us for a free consultation and find out how we can help you.
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