LIVIS Workflow

How LIVIS Works

From setup to continuous improvement, see how LIVIS powers smarter inspections.

01

Set Up

Configure Your Vision System

Seamlessly connect existing hardware or deploy new vision systems tailored to your line.

Integrate Existing Equipment

Connect current cameras, AOI, or X-Ray systems.

Install New Systems

Collaborate with integration partners for a tailored setup.

02

Model Training

Build Your AI Model

Seamlessly connect existing hardware or deploy new vision systems tailored to your line.

Save Time with Pre-Trained Use Cases

Access a curated library of 950M+ datasets and 700+ pre-trained use cases across surfaces and industries. 
Apply custom rules from defect size and count to presence checks to fit your exact inspection needs.

Simplify Dataset Preparation

Use auto-annotation, augmentation, and customizable tolerances to label defects quickly and accurately without extra effort.

Build Accurate Models Without the Overhead

Train with both supervised learning and anomaly detection to handle new parts, defects, and production lines.

Build Accurate Models Without the Overhead

Train with both supervised learning and anomaly detection to handle new parts, defects, and production lines.

03

Deploy

Deployment Architecture

Choose the integration model that fits your operations.

Cloud Integration

Centralized management across multiple lines and factories, with secure access anywhere.

On-Prem Integration

Local deployment for ultra-low latency and full control within your factory environment.

04

Inspect

Inspect Your Results

Inspect every unit in real time and turn results into actionable insights.

Detect More, Without Delay

Spot multiple types of defects in real time with high accuracy and no delay to production.
Operators can directly see issues on screen, ensuring confidence in every inspection.

Complete Traceability at a Glance

All PASS & FAIL outcomes are logged by date, time, and run session, giving you a clear record of every unit inspected.
Because results are stored with linked images and videos, you can easily confirm where defects occurred.

Insights That Drive Continuous Improvement

The dashboard provides a full view of defect data for deeper decision-making.
Perform root cause analysis, supplier grading, and predictive maintenance, and improve first pass yield, throughput, and OEE.

05

Improve

Improve Your Accuracy & Scale

Continuously improve accuracy and scale success across your entire factory network.

Keep Models Accurate with Continuous Feedback

Check inspection results and quickly re-annotate any misses to retrain models on the fly.
Whether right after training or during deployment, feedback ensures models stay continuously optimized.
No more black-box AI, every decision is explainable and transparent to your team.

Expand Across Lines and Factories with Ease

Easily register new lines without being locked to one type of hardware.
Reuse existing trained models or adapt them for new environments to scale faster across diverse setups.

Frequently Asked Questions

How many images or samples are needed for training the AI model?

Unlike most AI-based systems, LIVIS requires minimal data for training. Typically 30 samples per defect and 30 good samples are sufficient to develop the model.

Unlike most AI-based systems, LIVIS requires minimal data for training. Typically 30 samples per defect and 30 good samples are sufficient to develop the model.

Unlike most AI-based systems, LIVIS requires minimal data for training. Typically 30 samples per defect and 30 good samples are sufficient to develop the model.

Unlike most AI-based systems, LIVIS requires minimal data for training. Typically 30 samples per defect and 30 good samples are sufficient to develop the model.

Unlike most AI-based systems, LIVIS requires minimal data for training. Typically 30 samples per defect and 30 good samples are sufficient to develop the model.

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