AutoML is a fully automated, no-code tool designed to accelerate the pace of embedded machine learning development and scale. This platform provides an intuitive interface for labeling, recording, validating, and visualizing time-series sensor data.

AutoML supports a wide range of machine learning methods, tailored for applications requiring low latency, low power consumption, and small memory footprints. This allows companies to create powerful, efficient, and highly specialized embedded ML solutions at an accelerated pace.

Engineered AI for Enterprises takes a holistic approach to AI development, ensuring that the integration of artificial intelligence is not just about the technology but also about its alignment with your broader business goals. By considering both the technical aspects and the strategic needs of your organization, we create AI solutions that do more than just function—they drive meaningful change and deliver measurable results. This dual focus ensures that your AI initiatives are not siloed but are instead seamlessly woven into the fabric of your business operations, enhancing overall performance and competitiveness.

Our approach begins with a deep collaboration with your teams. We work closely with you to thoroughly understand your unique challenges, goals, and data ecosystems. This partnership allows us to tailor our AI solutions to fit the specific needs of your organization, ensuring that they are not only technically sound but also strategically impactful. By aligning our AI development with your organizational objectives, we create customized solutions that are designed to deliver significant, long-term value, enabling you to fully leverage the potential of AI to achieve your business goals.

AI-Powered Condition-Based Monitoring (CbM) systems revolutionize industrial operations by enabling continuous monitoring of machine states and real-time anomaly detection directly on the edge. These systems provide an unparalleled advantage by detecting issues as they arise, preventing costly downtime and extending machine life.

The true power of AI-Powered CbM systems lies in their ability to perform multi-class anomaly detection, identifying not just one but multiple types of irregularities that could indicate potential issues. Over time, as the system gathers more data, it can transition into Predictive Maintenance (PdM), where it proactively identifies early signs of potential failure. This capability allows maintenance teams to address issues before they escalate, leading to more efficient operations, reduced maintenance costs, and a significant reduction in unplanned downtime.