// PRODUCT / VISIONSTRATA PLATFORM

Every camera, one connected picture.

VisionStrata turns multi-camera video, thermal imagery and spatial sensor feeds into real-time operational intelligence, from the edge device to the cloud.

Manufacturing Security Smart cities Retail Logistics

Enterprise vision AI hits a wall at scale

Running computer vision across distributed sites brings computational, architectural and operational limits.

Bandwidth and latency in cloud-first video

Streaming raw high-resolution feeds from hundreds of remote cameras to a central cloud uses huge amounts of bandwidth and delays safety and inspection alerts.

Too many streams for CPU pipelines

CPU-based decoding and frame-by-frame analysis can't keep up with dense camera arrays. Frames drop and processing backs up.

Video cut off from other sensors

Visual feeds sit apart from thermal, acoustic and IoT telemetry, so teams struggle to get full situational awareness or reliable anomaly detection.

One GPU-accelerated vision layer

VisionStrata ingests, processes and analyzes multi-camera feeds at scale. Five layers work together, each built on the NVIDIA accelerated computing stack.

Video ingestion and decoding

Takes in feeds from industrial cameras, security systems and IoT sensors, with hardware-accelerated decoding that keeps the CPU free.

NVIDIA DeepStream

Multimodal vision analytics

Runs detection, classification, tracking, segmentation and thermal anomaly identification on continuous video, tuned for FP16 and INT8 inference.

NVIDIA TensorRT

Sensor fusion and spatial intelligence

Combines visual tracks with thermal signatures, depth data and environmental telemetry into one picture of the physical space.

NVIDIA RAPIDS

Model serving and orchestration

Deploys, scales and manages vision models across edge nodes and cloud clusters, with dynamic batching and concurrent multi-model execution.

NVIDIA Triton

Edge-to-cloud deployment

Runs time-critical inference at camera gateways and industrial assets while model management and fleet telemetry stay central.

NVIDIA Jetson

Build on the VisionStrata SDK

Use the TypeScript or Python SDK, or call the REST API directly, to connect streams, deploy models and subscribe to events from your own applications and dashboards.

  • Stream connectorsConnect RTSP, WebRTC and ONVIF sources with a single call.
  • Model deploymentPush TensorRT-optimized models to Jetson, L4 or cloud targets.
  • Fusion pipelinesAttach thermal, LiDAR and telemetry to any stream.
  • Event subscriptionsListen for anomalies, defects and safety events in real time.
$ npm install @visionstrata/sdk
$ pip install visionstrata
TypeScript@visionstrata/sdk
import { VisionStrata } from "@visionstrata/sdk";

const vs = new VisionStrata({ apiKey: process.env.VISIONSTRATA_API_KEY });

// Connect a camera with its thermal and LiDAR companions
const stream = await vs.streams.connect({
  source: "rtsp://dock-3.local/line-1",
  sensors: ["thermal", "lidar"],
  node: "jetson-orin-07",
});

// Deploy an optimized model to that edge node
await vs.models.deploy({
  model: "defect-detector",
  precision: "int8",
  target: stream.node,
});

stream.on("anomaly", (event) => {
  console.log(event.type, event.confidence, event.track.id);
});
Pythonvisionstrata
import os
from visionstrata import Client

client = Client(api_key=os.environ["VISIONSTRATA_API_KEY"])

stream = client.streams.connect(
    source="rtsp://dock-3.local/line-1",
    sensors=["thermal", "lidar"],
)

# Read fused anomaly events as they happen
for event in stream.events(type="anomaly"):
    print(event.type, event.confidence, event.track.id)

Built on the NVIDIA accelerated computing stack

Each NVIDIA technology has a clear job inside the VisionStrata platform.

Technology Primary function How VisionStrata uses it
NVIDIA DeepStreamVideo analytics and ingestionPowers high-throughput, GPU-accelerated multi-stream video analytics and spatial tracking.
NVIDIA TensorRTInference optimizationOptimizes computer vision and object detection models for low-latency production inference.
NVIDIA TritonModel servingManages concurrent serving of multi-model vision workloads with dynamic batching.
NVIDIA JetsonEdge AI computeRuns real-time vision inference and sensor processing locally on edge hardware.
NVIDIA RAPIDSGPU data analyticsAccelerates spatial metadata processing, feature engineering and time-series telemetry fusion.
NVIDIA MorpheusOperational cybersecurityMonitors vision stream pipelines and edge gateway payloads for security anomalies.

Compute for training, edge and serving

Different workloads run on the GPU best suited to them.

Cloud training and fleet management

Trains large multimodal vision foundation models, processes massive video datasets and manages fleet intelligence worldwide.

NVIDIA H100NVIDIA A100

Edge and enterprise nodes

Handles demanding multi-camera inspection and real-time edge vision inference at industrial and enterprise sites.

Jetson AGX OrinJetson Orin NX

Enterprise edge servers

Serves multi-model video analytics and keeps cloud and edge in low-latency sync.

NVIDIA L4NVIDIA L40S

From camera stream to decision

The intelligence core splits work between the cloud engine and local edge nodes.

Multi-camera streams and sensors

RTSP video, thermal imagery, LiDAR

VisionStrata Intelligence Core

DeepStream GPU ingestion and decoding
TensorRT analytics and inference
Triton serving and dynamic batching

NVIDIA H100 and A100 cloud engine

Model training and fleet analytics

NVIDIA Jetson edge and L4 nodes

Real-time local vision inference

Technical roadmap

Five milestones take VisionStrata from GPU ingestion to full sensor fusion.

  1. Q3 to Q4 2026

    DeepStream pipeline

    GPU-accelerated multi-stream ingestion and analytics processing.

  2. Q4 2026

    TensorRT optimization

    Detection and segmentation models tuned for low-latency edge execution.

  3. Q4 2026

    Triton model serving

    Scalable multi-model serving for concurrent vision and sensor workloads.

  4. Q4 2026 to Q1 2027

    Jetson edge deployment

    Real-time visual inspection models on Jetson AGX Orin hardware.

  5. Q1 2027

    RAPIDS sensor fusion

    Multimodal telemetry and spatial data correlation pipelines.

Turn your video streams into real-time spatial intelligence

Access to development repositories, API endpoints and demo environments is provided through enterprise single sign-on on request.