Vanti offers AI-based solutions for manufacturers that help optimizing the production process such as detecting failures early in the process, offering faster calibration process and detecting visual defects. Vanti provides Early Fault Prediction prediction analytics that turns data collected during the production process into a system that detects faults. This enables users to

See how many of units will pass and fail.
Identify the root cause of failure with pinpoint precision.
Automate or maintain complete control of the approval process.

Identifying and Preventing Defects Early in the Manufacturing Processes, Improving Quality and Eliminating Excess Waste

Vanti helps users automatically identify defects for improved scale, quality, and reduced cost across the entire manufacturing process without the need for image labels. With inspection data, Vanti helps gather more information from visual inspection data with purpose-built models. Users can:

Build and deploy models, automate or retain control of approvals
Identify systematic trends and groupings, learn what clusters are in images
Identify exactly what's going wrong with enhanced deficiency discovery

And through Process Tuning Time Optimization, manufacturing data can be used to predict performance values or vectors instead of quality control stations, speed up calibration, determine the optimal configuration for each process, and stop unnecessary processes early.

At its core, Vanti offers real-time prediction of units’ quality, based on data sent from the production line to Vanti servers. Prediction model is created based on historical data that is logged and extracted from the production line. Each model can serve one or more lines, depending on the data variability between the production lines. Once the model is deployed, an API integration enables real-time predictions. Once the needed data for model creation is in the possession of the user, the vendor states deploying a production-grade predictive analytics model takes only a few hours.

Vanti works behind the scenes to make sure a deployed model in production is the best fit. Data drifts, for example, could lead to a slow degradation of the model performance. So Vanti constantly collects data from the production line and automatically trains new models

based on this new data (this process is called “ghosts models”), in order to figure out if there is a model that could beat the performance of the deployed model. If Vanti finds a better model, it will automatically offer to upgrade a deployed model to a better version.

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