The Challenge
Deploying AI at the tactical edge is fundamentally different from cloud-based machine learning. Defense environments impose hard physical and operational constraints that rule out conventional inference architectures and demand purpose-built solutions.
Bandwidth Constraints
Tactical networks operate on low-bandwidth radio links where transmitting raw sensor data to a cloud inference engine is not feasible. Intelligence must be generated on-device before any data leaves the platform.
Latency Requirements
Target detection, threat classification, and C2 feed anomaly alerting all require sub-second response times. Round-trip latency to a remote server is operationally unacceptable for time-critical decisions.
Adversarial Environment
Electronic warfare, jamming, and active network interdiction can sever connectivity at any moment. AI systems must remain fully operational in fully disconnected mode with no degradation in core capability.
Disconnected Operation
Forward-deployed units operate for extended periods with zero connectivity. Models must be self-contained, locally updateable via OTA when connectivity resumes, and capable of running indefinitely offline.
Power Budget
Ruggedized edge platforms carry tight SWaP-C constraints. A model that runs efficiently on a data center GPU may be entirely unusable at 10W TDP. Quantization, pruning, and architecture selection must be hardware-aware from the start.
Model Integrity
Adversarial input attacks and model poisoning are real risks in contested environments. Deployed models must be validated, version-controlled, and cryptographically signed to ensure tamper resistance across the update lifecycle.
What We Build
Our defense edge AI development practice covers the full pipeline — from model selection and training through optimization, hardware validation, and operational deployment.
Computer Vision on Ruggedized Hardware
Object detection, classification, and tracking pipelines optimized for NVIDIA Jetson and Edge TPU hardware. Validated against real-world sensor inputs including EO/IR cameras and UAV payloads.
LLMs for Intelligence Triage
Quantized large language models deployed locally for OSINT summarization, threat report parsing, and intelligence triage — without requiring connectivity to external APIs or cloud inference endpoints.
On-Device Object Detection & Tracking
Real-time multi-object tracking with persistent identity across frames. Supports detection of vehicles, personnel, and UAVs under challenging conditions: low light, partial occlusion, and high clutter.
Anomaly Detection for C2 Feeds
Lightweight anomaly detection models that monitor command-and-control data streams for behavioral outliers, spoofed sensor inputs, and adversarial injection attempts in real time.
Federated Learning for Distributed Sensor Networks
Distributed training architectures that improve shared models across geographically separated edge nodes — without centralizing raw sensor data, preserving data sovereignty and OPSEC.
Model Optimization for Jetson & Edge TPU
Full optimization pipeline: INT8/FP16 post-training quantization via TensorRT and ONNX Runtime, structured pruning, knowledge distillation, and hardware-in-the-loop validation against target SWaP-C budgets.
Built With Corvus.Sense
Corvus.Sense — LLM-Based Cyber Threat Intelligence
Our edge AI engineering practice is not theoretical. Corvus.Sense is our production cyber threat intelligence platform that uses on-device and near-edge LLM inference to detect, classify, and track cyberattacks from open-source channels in real time — without exposing raw intelligence to external cloud providers. The same quantization and inference optimization techniques we apply to Corvus.Sense power the tactical AI pipelines we build for defense clients.
Corvus.Head, our battlefield C2 platform, also leverages edge AI for data fusion — correlating infantry, artillery, UAV, EW, and SIGINT feeds using ML-driven anomaly detection and pattern-of-life analysis running directly on forward-deployed hardware.
Explore Corvus.Sense →Our Approach
Defense edge AI projects fail when model development is decoupled from hardware constraints. We start with the target platform and work backwards through the model architecture — not the other way around.
- Mission analysis first. We map operational requirements to inference latency, power, and accuracy budgets before selecting a model architecture or training dataset.
- Hardware-in-the-loop from day one. Target hardware (Jetson, Edge TPU, or custom embedded platform) is part of the development environment from the first prototype, not an afterthought at deployment.
- Continuous validation against adversarial conditions. Models are stress-tested against sensor degradation, partial occlusion, adversarial perturbation, and spoofed input scenarios before delivery.