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Harness the power of Causal AI and accelerate your R&D with a cutting-edge AI library running your own hardware.
Process Booster X for R&D is an enterprise software designed to transform R&D workflows and accelerate innovation by harnessing the power of Causal AI. The highly autonomous and transformative AI technology enables a deep system-level understanding of cause and effect.
With our AI library, you run everything on your own hardware, giving you complete control over your data.
Using our SDKs—whether in Jupyter notebooks or other environments—you access the full power of Causal AI without needing to upload your data to any external platform. You stay in control, ensuring security and privacy while leveraging cutting-edge AI directly in your workflows.
Process Booster X for R&D offers a comprehensive suite of advanced AI engines, delivering actionable insights from raw data to inform decision-making and problem-solving. Each engine is designed to tackle unique challenges and deliver precise, actionable insights:
Automates the process of supervised learning by cleaning, sampling, and training models for both regression and classification tasks.
Reveals signal relationships to segment large systems into manageable units, unveiling the structural causal model.
Identifies the key drivers of KPIs to uncover stable conditions under which KPIs consistently perform better or worse.
Identifies when data trends deviate from expected patterns, signaling potential out-of-distribution scenarios.
Identifies the root causes of recurring negative events and provides real-time risks for them.
Manages and optimizes multiple KPIs and disruptions in parallel with AI countermeasures to mitigate impending incidents.
Learn more about these causal AI engines in our Technology section and in the documentation.
The causal AI powering Process Booster X uses a novel approach to Self-Supervised Learning leveraging Contrastive Learning - powered by Stochastic Differential Geometry. This approach 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.
Process Booster X for R&D is priced as monthly or annual subscription. Organizations can customize their solution to best meet their business goals.
Pre-compiled runtime binary of the Causal AI engines for on-premise use.
Pre-compiled runtime binary of the Causal AI engines for on-premise use.
Language specific SDKs (Python, Java, C) to use the engines.
Pre-configured Docker images for common R&D processes.
A toolkit for visualizing scientific results like analyses and model evaluations.
License keys provided for flexible usage across multiple users, systems or environments.
How often one of the AI engines can be used.
Models can be inferred with live data for testing purposes.
Models can be inferred with live data for production.
HTML-based documentation about all AI engines including examples.
Availability of our AI experts for questions via email and online meetings.
Full focus on Causal AI based analytics for root cause analyses, debugging, and more for accelerated product development.
Full focus on Causal AI based analytics and live model inferences in test & productive environments.
