State-of-the-art AI,
optimized for the edge.

Built on NVIDIA Jetson with TensorRT, DeepStream, and custom ML models. All processing happens locally for maximum privacy and speed.

Edge Inference on NVIDIA Jetson

Our appliances use the NVIDIA Jetson platform for on-device AI. Jetson modules (Orin Nano, Orin NX, AGX Orin) offer hundreds of teraflops of performance with only tens of watts of power.

For example, Jetson Orin NX delivers up to 100 TOPS of INT8 inference, far beyond CPU capabilities. This means we can run multiple deep neural nets in parallel — a YOLOv8 object detector on camera feeds and an autoencoder anomaly detector on network flows at the same time.

Why It Matters

All processing happens locally — sensitive data never leaves your site
Decisions happen in real-time without cloud round-trip
Reduces bandwidth cost — only metadata and alerts are sent externally
Sub-50ms latency on IoT network flow analysis

Orin Nano

~40 TOPS · 7–15 W · 8 GB

Orin NX

~100–157 TOPS · 10–25 W · 16 GB

AGX Orin

~200–275 TOPS · 30–60 W · 32–64 GB

Multi-Camera Security Example

Deploy 2–4 cameras with Blueural Edge. Our pre-trained neural nets detect:

Person without helmet in factorySafety Alert

Group loitering near restricted exitSecurity Alert

Unauthorized entry + network anomalyCorrelated Alert

Multi-Sensor Video Analytics

Blueural leverages NVIDIA DeepStream (a GStreamer-based AI toolkit) for camera and sensor integration. DeepStream is an open-source framework for multi-camera video analytics: tracking, object recognition, 3D multi-camera tracking.

We use it to add visual context — identify an unfamiliar person accessing a sensitive area and correlate it with a network alert. Complex vision tasks run on Jetson with minimal code.

Object DetectionFace Recognition3D TrackingZone IntrusionPPE Compliance

Machine Learning Models & Data

Network Flow Models

Gradient-boosted and neural classifiers trained on public flow corpora such as CIC-IDS, then benchmarked on edge hardware for latency as well as accuracy.

Behavioral Profiling

Unsupervised autoencoders learn your normal traffic patterns to catch zero-day threats.

Computer Vision

Optimized YOLO/TensorRT for object detection. NVIDIA TAO Toolkit models for rapid retraining.

Generative AI

Small LLMs on Jetson Orin for log summarization and natural-language querying (Roadmap).

AI Architecture Pipeline

Ingest

Packets and sensor data flow into device

Preprocess

Intelligent filtering to reduce noise

Inference

GPU-accelerated models analyze in parallel

Fusion

Decision engine combines vision + flow scores

Output

Human-readable alerts with context

State-of-the-art AI,optimized for the edge.