Jetson and edge GPU targets
For scenarios requiring heavier inference loads, multiple cameras or high FPS.
- Industrial inspection
- Security analytics
- Multi-camera retail analytics
Technical trust layer
Edge AI runtime, deployment and privacy architecture
Technology page; It has been rewritten to provide clear answers to the questions "which stack does it work with?", "can it be done without the cloud?", "which devices can it be deployed on?" and "how to monitor the operation".
C++ and OpenCV
ONNX Runtime
local interference
Jetson / RPi / edge PC
Core runtime logic
Infereal deployments typically consist of camera input, preprocess, model inference, business logic and dashboard / event stream layers.
- Capture layer: Pipeline selection suitable for camera stream, frame sampling and field conditions.
- Inference layer: OpenCV, ONNX Runtime or other optimized runtime options as needed.
- Business logic: Detection, counting, intrusion, QA, access event, enhancement or kiosk interaction flows.
- Output layer: Dashboard, export, notification or local log mechanism.
Why is this statement important?
Search and AI systems look for architectural clarity, not abstract slogans, on technical pages. This episode provides exactly that.
See the deployment processDeployment targets and device profiles
The same model will have different trade-offs on different devices. That's why device fit has become one of the main parts of the technology page.
Raspberry Pi and light edge setups
For more limited but cost-oriented or more compact deployment needs.
- light weight counting
- Simple kiosk or trigger tasks
- Local utility workflows
Kiosk and mixed-interface setups
For event experiences, selfie streams, branded interactions and deployments requiring local UI.
- mobile companion and local capture
- Demo / prototype surfaces
- web dashboard can work together with
Privacy-first architecture
For a corporate visitor looking for “video analytics”, privacy and governance language may be decisive. That's why it's clearly on the page.
- Thanks to Inference's ability to work in the field and on the device, control of the data flow increases.
- Facial recognition or cloud sync is not mandatory in every scenario; The decision is made according to the minimum data flow suitable for the use case.
- Dashboard and export layers can be configured in a local or controlled central structure depending on operational need.
operations layer
Once deployment begins, the following elements ensure that the system produces real business value:
- Monitoring and event visibility
- Versioning and updating rhythm
- Export, alarm and dashboard standardization
Frequently asked technology questions
This section was added to support question-based searches and answer surfaces on the technical SEO side.
What technology stack does Infereal use?
It uses a combination of C++, OpenCV, ONNX Runtime, web dashboard and local event / export layers depending on the project requirement.
Can a dashboard be installed without cloud?
Yes. While inference remains local in many deployments, the dashboard layer can be positioned locally or within a controlled network.
Which device to choose for Edge deployment?
Jetson, edge PC, kiosk or more compact devices are selected according to the number of cameras, FPS, model weight, physical space and operation budget.
Why is the Playground a separate surface?
Demo, prototyping and technical proofs of concept; It requires a different experience than live field deployment. Therefore it is kept as a discrete surface.
Let's connect technology decisions with use cases
If your technology language is clear but your business scenario is vague industry pages; If the use case is clear but the architecture is unclear, this page and contact is the best next step.