AI and data analytics

Rapidminer supports advanced analytics through its distributed, in-memory, massively parallel processing (MPP) graph query engine. This engine facilitates OLAP-style analytics directly on the universal data overlay. Its scalable MPP architecture enables users to pose OLAP-style questions across diverse enterprise data sources within the knowledge graph. This can be done using SPARQL, the HiRez visual analytics framework, or through large language models (LLMs) via our MCP server. We also offer the Gartner MQ-leading Rapidminer AI Studio and AI Cloud for data science and machine learning. These tools support both guided, directed construction of predictive models with visual drag-and-drop workflow editors and coding data scientists working in notebooks and IDEs. A common framework underpins all design experiences, providing unified security, resource management, logging and operationalization. This integrated approach allows subject matter experts (SMEs) and data scientists to collaborate effectively on projects, each utilizing their preferred authoring environment, while Rapidminer natively and seamlessly integrates their code and low-code assets.

Rapidminer is built on a robust, distributed and containerized microservice architecture. It leverages technologies such as Docker, Kubernetes and OpenShift to efficiently scale workloads across hundreds or thousands of CPU cores, GPU cores and terabytes of RAM. Furthermore, Rapidminer AI Studio and Hub can integrate with PBS and HPC platforms, including those powering some of the world’s largest supercomputers and HPC clusters. Every component of the Rapidminer architecture—from Rapidminer Graph Studio and Lakehouse MPP architecture to Rapidminer AI Hub and AI Cloud job agent architecture—operates at cloud-scale, effectively future-proofing any organization’s AI initiatives.

To mitigate the risk of hallucinations in large language models (LLMs), Rapidminer establishes a direct connection between LLMs and agents and your enterprise data. This connection is facilitated by ontologies and a graph data overlay. Consequently, inquiries regarding data trigger direct queries against the actual data, thereby preventing the generation of memorized or fabricated responses. The resulting queries are transparent and inspectable, offering clear insight into agent operations. Rapidminer AI Cloud also provides purpose-built tools that impart specific behaviors and functionalities to agents, complemented by comprehensive tracing and logging. This ensures a complete understanding and auditable record of how results are produced by agents developed using Rapidminer software.

Enterprises thrive on data – the recorded information that underpins every function and system, enabling operation and innovation. This data is continuously generated within the systems that power business processes, from product and service design through manufacturing. This vast ocean of data holds immeasurable value. When data analytics are applied, profound insights are unlocked, driving better decisions and more efficient processes. The intelligence extracted from these analytics is truly transformative.

To harness this potential, our software creates a universal data overlay. This powerful tool integrates and semantically enriches all enterprise data scattered across numerous source systems, each supporting a distinct business process. This integration and enrichment are key because they allow us to ask questions that span across different domains and business functions, providing a holistic view. Ultimately, a universal data overlay is the essential foundation for any effective data analytics capability.

Building on this foundation, our intelligence layer empowers us to develop specific analytics that an organization needs to make superior business decisions. These analytics can take various forms:

Descriptive analytics, such as a sales dashboard, provide a clear picture of past performance, including sales effectiveness and revenue from recent deals.

Predictive models can inform a plant operator precisely when to replace a critical part, preventing costly unplanned failures. Prescriptive models can even automatically set optimal parameters for product manufacturing, ensuring peak efficiency.

In essence, we view data analytics, integration, and intelligence as a seamlessly integrated set of capabilities. These are absolutely essential for driving data-driven decisions throughout an enterprise. That’s why we’ve built a comprehensive platform for our customers that encompasses all these aspects, providing enterprise-level scalability and robust governance.