

Move 100s of TB of data to Oracle Cloud Infrastructure in days, instead of weeks or months.

Oracle Utilities Meter Data Management gathers, processes, and stores all types of meter data to help utilities deal with rising data volumes and succeed in a changing industry.

Oracle Cloud Infrastructure (OCI) Generative AI is a fully managed service that provides state-of-the-art, customizable large language models (LLMs) designed to address a wide range of enterprise use cases, including text generation, summarization, and embedding creation. This service enables organizations to seamlessly integrate advanced language comprehension capabilities into their applications, enhancing productivity and decision-making processes.

Oracle offers a c loud-enabled customer experience (CX) solution; differentiate your company across all channels, touch points, and interactions.

Cloud-based, globally distributed cybersecurity platform

Oracle Internet Application Server is a middle-tier application server designed to enable scalability of web and database-centric applications.

Oracle Intelligent Data Lake is an integral component of the Oracle Data Intelligence Platform, designed to unify and streamline data management by integrating diverse data sources into a cohesive environment. Leveraging open-source standards such as Apache Spark and Apache Flink, it facilitates advanced data processing and real-time analytics. The platform offers a unified developer experience, incorporating a comprehensive data catalog and Jupyter Notebook for in-depth data analysis and visualization. With robust security measures, including fine-grained, role-based access controls, Oracle Intelligent Data Lake ensures secure data storage and sharing, effectively eliminating data silos and enhancing decision-making processes.

Oracle Real User Experience Insight enables enterprises to maximize the value of their business-critical applications by delivering insight.

Oracle AI Vector Search, introduced in Oracle Database 23ai, empowers organizations to perform AI-driven similarity searches directly within their existing database infrastructure. By integrating vector search capabilities natively, it eliminates the need for separate vector databases, thereby reducing complexity and enhancing security. This functionality enables semantic searches across both structured and unstructured data, facilitating more sophisticated AI applications. Additionally, it supports retrieval-augmented generation (RAG), allowing large language models (LLMs) to deliver more accurate and contextually relevant results by leveraging enterprise data. Key Features and Functionality: - Native VECTOR Data Type: Store vector embeddings directly within tables, supporting various dimension counts and formats to accommodate different embedding models. - Flexible Vector Generation: Import embedding models using the ONNX framework or utilize database APIs to generate vectors from preferred embedding services. - Vector Indexes: Accelerate similarity searches with specialized indexes, such as in-memory neighbor graph indexes for high performance and neighbor partition indexes for large datasets. - Intuitive SQL Querying: Perform similarity searches using simple SQL queries, seamlessly combining vector data with relational, text, JSON, and other data types. - Retrieval-Augmented Generation (RAG): Enhance LLM interactions by providing context-specific private data, improving the accuracy of responses through combined similarity and business data searches. - Industry-Leading Security: Leverage Oracle's robust security features, including encryption, data masking, and access controls, to protect data while utilizing advanced AI search capabilities. Primary Value and User Benefits: Oracle AI Vector Search addresses the challenge of integrating AI-powered similarity search into existing business data systems without the overhead of managing multiple databases. By embedding vector search capabilities directly into Oracle Database, it simplifies application development, enhances data security, and ensures consistency. Users can perform semantic searches across diverse data types, leading to more relevant and accurate insights. Furthermore, the support for RAG enables organizations to improve the performance of LLMs by grounding them with enterprise-specific data, reducing inaccuracies and enhancing decision-making processes.


Oracle Corporation develops, manufactures, markets, hosts, and supports database and middleware software, applications software, and hardware systems.