Scalable RAG solution launched for organisational use

Progress Software, a provider of digital experience and infrastructure software, has announced the launch of Progress Agentic RAG, a SaaS-based Retrieval-Augmented Generation (RAG) platform designed to support the use of generative AI grounded in organisational data. The platform is intended to help organisations of various sizes apply Large Language Models (LLMs) in a more reliable and verifiable manner by drawing from their own business content.

This release builds on Progress’ existing data management, retrieval, and contextualisation tools, which aim to help organisations gain more value from both structured and unstructured data. Businesses increasingly face the challenge of managing growing volumes of content in multiple formats and languages. Extracting insights from documents, video, audio, and other data sources can be time-consuming and complex, especially when content is scattered or siloed across systems.

While generative AI offers new capabilities, without the proper business context, outputs can be inaccurate or irrelevant. In addition, most existing RAG solutions require substantial technical knowledge and infrastructure to implement effectively. Progress Agentic RAG addresses these concerns by offering a simplified deployment model through a software-as-a-service approach, with built-in traceability and support for a wide range of content types and languages.

According to Progress CEO Yogesh Gupta, the platform is designed to change how organisations interact with their internal data. By combining agent-based processing with retrieval-augmented generation, it seeks to provide scalable and transparent AI functionality without requiring extensive custom development or resources.

Patrick Garcia, Chief Digital, AI & Innovation Officer at SRS Distribution, noted that adopting the platform, originally implemented under the name Nuclia, has significantly improved how the company accesses and uses internal information. He pointed to improved efficiency and more reliable insights from unstructured data as key outcomes.

Progress Agentic RAG enables ingestion, indexing, retrieval, and search across multilingual text, audio, video, and other content formats without the need for custom coding. It integrates with various enterprise-ready LLMs, allowing users to select the model that best suits their requirements. The platform is powered by NucliaDB, a purpose-built database that supports vector storage, semantic and keyword search, metadata querying, knowledge graph traversal, and multimodal data interpretation. In addition, it includes built-in tools for evaluating and maintaining the consistency and traceability of responses, referred to as RAG Evaluation Metrics (REMi).

Amy Machado, Senior Research Manager at IDC, commented that agentic AI is increasingly being used to support operational decision-making and information access. She highlighted that platforms aiming to reduce the complexity of deployment, such as Agentic RAG, may play a role in making generative AI more broadly available to organisations regardless of size.

The platform is already being applied across various sectors. Organisations are using it to recommend products during sales processes, automate customer service through contextual responses, assist legal teams with research, preserve institutional knowledge, support training and onboarding, and enable secure querying of sensitive or complex data sets.

Progress Agentic RAG is now available as a self-service solution through the AWS Marketplace and Progress.com. Pricing begins at USD 700 per month.