AI-powered Progress Software has announced the launch of Progress Agentic RAG, a SaaS Retrieval-Augmented Generation platform for UK financial services.
Following this announcement, the platform is set to make trustworthy and verifiable generative AI accessible across organisations and teams of all sizes. This launch marks an important step in affordable AI development for organisations that are looking to get the benefits of Large Language Models (LLMs) with results based on real business data.
In addition, the new platform is expected to expand Progress’ portfolio of end-to-end data management, retrieval, and contextualisation solutions that allow businesses to leverage all their data to gain a competitive edge. The financial institution is expected to continue to focus on meeting the needs, preferences, and demands of clients and users in an ever-evolving market, while prioritising the process of remaining compliant with the regulatory requirements and laws of the industry as well.
More information on Progress Software’s launch of Progress Agentic RAG
According to the official press release, all businesses contend with growth in unstructured and structured data across different formats and languages. In order to improve business outcomes and productivity, organisations, teams, and individuals seek to extract insights from an array of documents, videos, audio, and other disparate sources that usually remain siloed or hard to find. Furthermore, GenAI without the context of all available business data leads to unreliable answers that aren’t useful to organisations. In addition, traditional RAG offerings usually require sophisticated expertise and significant resources to implement and run. With this in mind, Progress Agentic RAG will focus on delivering traceable and intuitive GenAI-powered search that is simple to set up, easy to use, and affordable for businesses of any size.
Included in the key features of Progress Agentic RAG platform are the No-Code RAG Pipeline (an agent-powered and streamlined ingestion which indexes and retrieves across multilingual text, audio, video, and other formats), Intelligent Search (enables the delivery of AI search and generative answers on top of unstructured data, providing knowledge in the form of trusted answers), and Deploy AI agents (featuring only RAG platform specifically designed to deliver reliable and scalable retrieval functionalities to AI agents), as well as Multi-Model Integration (which was developed in order to support all enterprise-ready Large Language Models (LLMs), offering full user control over the choice of LLM), Purpose-built Database for RAG (underpinned by NucliaDB, which, in addition to storing vectors, has also built-in semantic search, keyword search, metadata search, knowledge graph traversal and multi-modal understanding in order to produce coherent, trusted, human-grade answers), and RAG Evaluation Metrics (REMi – developed to support traceability and consistent answer quality).
