⚡ Real-Time Spatial Intelligence

Vision & Perception Engine

Industrial-grade computer vision with sub-15ms latency. Detect, track, segment, and understand complex environments in real-time across edge and cloud deployments.

View SDK & API → Performance Benchmarks

Person: 98%

Vehicle: 96%

Hazmat: 94%

Core Capabilities

Multi-Modal Visual Intelligence

Production-ready perception modules optimized for accuracy, speed, and hardware efficiency.

🎯

Object Detection & Tracking

Multi-class detection with persistent tracking across frames. Supports 500+ classes with configurable confidence thresholds.

🧩

Semantic Segmentation

Pixel-level scene understanding for autonomous navigation, medical imaging, and quality inspection workflows.

🌐

3D Scene Reconstruction

Stereo vision and depth estimation pipelines generating real-time point clouds and spatial meshes.

🔍

Anomaly Detection

Unsupervised learning for defect detection, safety violations, and irregular pattern recognition in industrial settings.

📡

Multi-Camera Fusion

Seamlessly stitch and correlate feeds from IP cameras, LiDAR, and thermal sensors into unified spatial maps.

🌙

Low-Light & Night Vision

Enhanced tensor processing for high-contrast extraction in suboptimal lighting and adverse weather conditions.

Pipeline Architecture

From Pixels to Decisions

A streamlined inference pipeline optimized for deterministic latency and throughput.

Feed Ingestion

RTSP, USB, IP cameras, & synthetic data streams

Preprocessing

Normalization, ROI cropping, & augmentation

Neural Inference

TensorRT / ONNX runtime optimization

Spatial Fusion

Tracking, depth mapping & context layering

Event Output

JSON, MQTT, WebSocket, or PLC triggers

Applications

Deployed Across Industries

Scalable perception models adapted for domain-specific challenges.

🏭

Manufacturing QC

Automated defect detection on assembly lines with 99.2% false-positive reduction and real-time rejection triggering.

🤖

Autonomous Robotics

Obstacle avoidance, path planning, and dynamic environment mapping for warehouse and outdoor robots.

🏬

Smart Retail & Logistics

Shelf monitoring, queue analysis, and automated inventory tracking with privacy-compliant processing.

🏥

Medical Imaging Assist

ROI highlighting, measurement automation, and preliminary anomaly flagging for radiology workflows.

Performance

Benchmarked & Verified

Independent testing on standard datasets and real-world deployments.

<15ms

End-to-End Latency

94.7%

mAP @ 0.5:0.95

120 FPS

Max Throughput (GPU)

82 MB

Optimized Model Size

Developers

Integrate in Minutes

Language-agnostic SDKs with comprehensive documentation and sandbox environments.

vision_client.py

from nexusai.vision import PerceptionClient

# Initialize client with API credentials
client = PerceptionClient(api_key="nx_live_8f7...")

# Load optimized detection model
model = client.load_model(
    name="nexus-vision-v2",
    backend="tensorrt",
    device="gpu:0"
)

# Process video stream in real-time
for frame in client.stream("rtsp://camera_01/feed"):
    results = model.detect(frame, confidence=0.85)
    
    for obj in results.objects:
        print(f"[{obj.class}] {obj.conf:.2f} @ {obj.bbox}")
        
    # Trigger downstream automation
    if obj.class == "defect":
        client.trigger_plc("LINE_A_STOP")
                    

Ready to Deploy Perception AI?

Get sandbox access, evaluate our vision models with your dataset, and deploy to production in days.

Request Early Access → Read Documentation