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No data preparation or data science expertise required.
aivis® is a game-changing causal AI engine that solves the hardest issues in product development and production environments without requiring data science work or prior domain knowledge - saving two extra steps! This makes it much faster than others and dramatically increases the success rate for AI-based problem-solving. aivis® powers our solutions Process Booster X and Process Booster X for R&D.
aivis® is a cutting-edge AI technology that goes beyond conventional prediction-based methods. While traditional AI models, such as neural networks and large language models, excel at identifying correlations, they often fall short in understanding the "why" behind these patterns. aivis® changes this by leveraging Causal AI to distinguish between cause and effect, enabling deeper insights into the true dynamics of complex processes.
aivis® is powered by a combination of Causal AI and Self-Supervised Learning, using advanced techniques such as Contrastive Learning and Stochastic Differential Geometry. This unique approach allows aivis® to autonomously process large volumes of raw data, rapidly building highly efficient, transparent, reports and white-box models without the need for extensive human input or pre-trained data. This autonomy makes aivis® particularly adept at handling thousands of process variables and delivering actionable insights across a wide range of industrial applications.
Unlike standard models that predict outcomes based solely on correlations, aivis® goes a step further by simulating "what if" scenarios, enabling a full understanding of internal process dynamics. This ability to explore different process adjustments allows aivis® to predict how the system will react to changes and to provide precise, effective countermeasures. This way, aivis® ensures that disruptions are understood and prevented, keeping processes running smoothly and efficiently.
By understanding not only what might happen but also why it will happen and how to respond, aivis® is transforming processes and operations with unparalleled precision and intelligence.
Want to go even deeper? Then download our technology white paper:
aivis® is a comprehensive suite of advanced AI engines, each meticulously designed to tackle specific challenges in your operations. Whether it's optimizing processes, predicting key signals, or detecting anomalies, every engine within the Aivis suite delivers high-performance solutions tailored to your unique needs.
Automates the process of supervised learning by cleaning, sampling, and training models for both regression and classification tasks.
The engine uses advanced mathematical techniques, like multivariate and non-linear statistics (Geometrical Kernel Machines), to simplify user input and achieve high-quality predictions. During training, the engine builds a model from historical data, applying automatic steps such as time synchronization, feature engineering, and selection. This model can be used in real-time to predict the target signal's current value based on recent data.
Reveals signal relationships to segment large systems into manageable units, unveiling the structural causal model.
The engine enhances your understanding of signal interactions by clustering similar signals based on their behavior. Signals that behave alike are grouped together, with the cluster's center representing the most typical signal. This approach is particularly useful as a preparation step for Aivis Anomaly Detection, helping you identify the most representative target signals within a cluster, ensuring more accurate and efficient anomaly detection.
Identifies the key drivers of KPIs to uncover stable conditions under which KPIs consistently perform better or worse.
By analyzing relationships between the target data and other inputs, the engine provides a detailed breakdown of how to optimize goals like KPIs and reveals the causes of suboptimal performance. What sets this engine apart is its ability to offer clear, actionable instructions based on this analysis, making it highly explainable compared to conventional machine learning models. Enhanced by advanced mathematical improvements over traditional decision and regression tree models, it delivers cutting-edge insights while minimizing user input and configurations.
Identifies when data trends deviate from expected patterns, signaling potential out-of-distribution scenarios.
The engine learns normal behavior directly from the raw data. By tagging a target signal, it identifies relevant signals and monitors them in context, enabling early detection of even small deviations. Anomalies can be classified and tagged to refine the model for specific components, with incremental updates to expand the model over time. Using advanced techniques, it delivers fast, accurate results with minimal training data, allowing easy deployment of multiple models.
Identifies the root causes of recurring negative events and provides real-time risks for them.
The engine focuses on root cause analysis, helping to understand the underlying reasons behind even the most complex recurring events. By analyzing historical incidents, it classifies events by root cause, offering clear insights into why problems occur. This allows for more effective detection, understanding, and prevention. Powered by advanced mathematical techniques, the engine provides high-precision results with minimal user input. Based on that input, it creates models that deliver real-time risk scores for each root cause, enabling proactive monitoring.
Manages and optimizes multiple KPIs and disruptions in parallel with AI countermeasures to mitigate impending incidents.
The engine integrates several aivis® models to create a master model that helps maintain system stability by satisfying key constraints. The engine works by finding the closest configuration in feature space that meets all given constraints. This can be applied to any system where KPIs need to stay within specified thresholds while minimizing disruptions. The engine orchestrates sub-models, such as signal prediction, anomaly detection, and state detection, into a master model. It optimizes within this space, ensuring all constraints are met while maintaining the desired system performance.
aivis® processes thousands of sensor signals and terabytes of raw data with ease and is at least 10 times faster than conventional approaches. It offers decisive advantages compared to conventional AI approaches:
Directly processes raw, unfiltered time-series data without requiring data cleaning, transformation, or pipeline setup.
Automatically manages data cleaning, filtering, labeling, and feature engineering, without prior domain knowledge.
Data analysis and model creation takes minutes or hours compared to the traditional timeline of weeks or even months.
While huge amounts of data can be processed, even very small amounts of data are sufficient to achieve excellent results.
The AI is highly hardware-efficient, running smoothly on CPUs vs costly GPUs.
Model inferences are very lightweight, running on edge devices much more effectively vs neural networks.
These differentiators drastically shorten the time to insights and conclusions, offering a much faster alternative to traditional data analytics methods.
