1
Collect & Train
Curate domain-specific datasets from field deployments. Train vision, audio, and sensor fusion models with edge constraints baked in from day one.
From training to on-device inference — Decision Labs Edge delivers optimized models, a unified SDK, and a catalogue built for drones, wildlife monitoring, and environmental sensing in the field.
An end-to-end edge ML pipeline — from data collection in harsh environments to optimized models running on-device with full telemetry and OTA update support.
Curate domain-specific datasets from field deployments. Train vision, audio, and sensor fusion models with edge constraints baked in from day one.
Quantize, prune, and distill models for target hardware. Profile across NPUs, GPUs, and MCUs to hit your latency and power budget.
Export to platform-native formats through our unified build system. Version, sign, and bundle models with runtime configs and pre/post-processing pipelines.
Ship to fleets of drones, camera traps, and sensor nodes. Monitor drift, push OTA updates, and close the loop with edge-collected feedback.
The Decision Labs Edge SDK abstracts hardware differences so your team ships faster — from mobile apps to bare-metal embedded systems running in the field.
Swift bindings with Core ML and ANE acceleration. Camera pipeline integration for real-time vision on iPhone and iPad field apps.
Swift · Core ML · VisionKotlin/Java SDK with NNAPI and GPU delegates. Background inference for rugged tablets and custom Android-based edge devices.
Kotlin · TFLite · NNAPIHigh-performance native runtime for Linux edge servers, NVIDIA Jetson, and custom SBCs. Zero-copy camera buffers and batch inference.
C++17 · ONNX Runtime · TensorRTMicrocontroller targets with CMSIS-NN and custom kernels. Run detection and classification on ARM Cortex-M and RISC-V at milliwatt budgets.
C · CMSIS-NN · FreeRTOSContainerized deployment for gateway devices. gRPC and MQTT interfaces for sensor fusion hubs and drone companion computers.
Docker · gRPC · MQTTBrowser and Node.js inference for dashboards, annotation tools, and rapid prototyping before hardware deployment.
WASM · ONNX.js · TypeScriptPre-trained and fine-tunable models optimized for low power, offline operation, and harsh environmental conditions.
Real-time object detection for aerial and ground cameras. Optimized for 640×640 input on Jetson Nano and mobile NPUs.
Species classification from camera trap imagery. Trained on diverse lighting, occlusion, and motion blur conditions.
Semantic segmentation for forest canopy, water bodies, and land cover from drone orthomosaics and satellite downlinks.
Person and vehicle detection in thermal/IR feeds for search-and-rescue drones operating at night or through smoke.
On-device bird, bat, and mammal call classification from passive acoustic monitors in remote habitats.
Detect rotor signatures and environmental sound events for collision avoidance and wildlife disturbance monitoring.
Multivariate anomaly detection on PM2.5, CO₂, and VOC sensor arrays for urban and industrial monitoring nodes.
Predict contamination events from turbidity, pH, conductivity, and temperature fusion on floating sensor buoys.
Human and vehicle detection in protected areas with edge-triggered alerts — runs offline on solar-powered camera traps.
Decision Labs Edge powers autonomous systems where connectivity is unreliable, latency is critical, and every watt counts.
Onboard inference for obstacle avoidance, target tracking, crop health mapping, and SAR thermal search — processing 4K video streams in flight with sub-50ms latency.
Camera traps and acoustic sensors that classify species, count populations, and alert rangers to poaching activity — all without cellular connectivity.
Distributed sensor networks for air quality, water contamination, deforestation, and coral reef health — with edge analytics that filter noise before uplink.
Talk to us about custom model training, SDK integration, or deploying our catalogue on your hardware fleet.