Creating Contextual Human-Centric AI to Drive Business Outcomes

Progress empowers organizations to achieve transformational success in the face of disruptive change. Our software enables customers to develop, deploy and manage responsible, AI-powered applications and experiences via products that both enable AI and include AI functionality themselves. Customers are actively using Progress AI-powered products to go from research projects to production-ready systems, leveraging AI to accelerate business value. Read on for answers to common questions about the Progress Data Platform for AI.

The key is context. If you give generative AI the right context in its prompt (token window), you can solve many of the problems associated with generative AI. This includes hallucinations, data freshness, biased data and so on. Finding the “right” context requires the use of all the different kinds of querying mechanisms at your disposal, including vectors, graphs, key values, tables, geospatial and more.

Progress RAG outperforms other enterprise AI implementations by providing superior contextual assistance through easily tunable queries, tailored to specific use cases, roles or individual users. It enables the reuse of the same data across various applications, enhancing versatility and efficiency. Additionally, its Progressive Vectors significantly cut vectorization costs by 4 to 10 times, depending on data usage patterns. Finally, Progress RAG facilitates the swift discovery and completion of missing data within your graph through its Progressive Graphs for a more comprehensive and accurate dataset.

Trusted enterprise-grade RBAC and QBAC (query-based access control), element-level security allows generative AI ONLY to see the data their users are allowed to see. Progress has a long history with government organizations using technology such as Progress MarkLogic to store and use data under the strictest of conditions.

The Progress Data Platform supports a variety of architecture types and use cases, extending beyond just AI. It includes RAG for generative AI, enabling advanced capabilities in artificial intelligence applications. Additionally, the platform offers semantic search for enterprise data, allowing for more accurate and relevant search results across extensive datasets. It also supports object modeling, which provides a robust framework for effectively representing and managing complex data structures. This versatility empowers the Progress Data Platform to meet diverse business needs and technological requirements.

Agile semantic graphs can be used to validate responses. Citations and links to the graphs and content with user feedback stored within the Progress Data Platform help validate missing semantics. Our Progressive Graphs enable automated semantic candidates based on users’ usage patterns.

We have customers scaling to petabytes of secured data using our shared-nothing architecture. We support some of the largest data use cases—both on-premises and in cloud environments—with millions of queries into these large data sets, every second of the day, across our enterprise customers.