Meet AiKno® – your answer to all things Industrial AI.
At LTTS, we have been laser focused on building AI solutions for industrial use-cases. Our machine learning library, natural language processing capabilities, and machine vision computing abilities, are engineered to work in some of the harshest of conditions. Take a look at how we can help.
The AiKno® Predictive analysis framework provides real-time visibility into your equipment health and detects anomalies or failures long before they actually happen. With the help of our in-built AI/ML models, service requests are automatically triggered or machines run self-diagnosis programs to fix issues
Our framework has the capability to automatically preprocess, run various ML algorithms, and compare the models on the basis of key metrics. It can automatically select the best possible model and reduce the manual effort of creating the models
For a major mining company, we developed an AI model to predict the % of silica in raw materials with a high degree of accuracy. This significantly improved the operational efficiency and reduced raw material wastage.
Traditionally, developers (or operators) often inspect their machine logs manually with keyword searches and rule matching. The increasing scale and complexity of modern systems, however, has caused the volume of these logs to explode and rendered manual inspection unfeasible.
To eliminate manual effort & human errors, AiKno uses automated log parsing algorithms (structuring the raw log data) and machine learning techniques such as anomaly detection to capture erroneous logs, which then helps in quick troubleshooting of the devices.
As a case in point, our log analysis was able to analyze unstructured logs in ultrasound machines and convert them to a structured format. This helped in identifying the log patterns, predicting the fault type, and taking preventive corrective action.
The AiKno® sentiment analytics module helps you understand your customers better and enhance user experience. Domain related sentiments are drawn from textual statement data available in various csv, log, and product information files. By applying deep learning models, we are able to gather together important sentimental insights that you can apply to your product & marketing strategy.