Artificial Intelligence at Progress

At Progress, we are taking a responsible and agile approach to Artificial Intelligence. We implement AI by first establishing rigorous processes to evaluate how AI models or applications function, where data flows and what protections are in place—both from the models and third-party integrations. We maintain compliance with industry standards by collecting necessary attestations from our engineers regarding product components. This disciplined approach enables us to confidently launch advanced AI capabilities, delivering productivity gains while prioritizing safety and responsibility for our customers and the industry. This is central in our approach to AI, not just with ourselves but with our customers, under the pillars of trust, accuracy, security and scalability.

A Large Language Model (LLM) is a type of artificial intelligence that can process and generate natural language, such as text or speech. LLMs are trained on huge quantities of data, such as web content, published works and periodicals. From that, the system learns the patterns and rules of language and can effectively predict words to complete sentences, answer questions, summarize text and compose lengthy responses. Examples include GPT-4 and BERT, used in applications such as chatbots and automated content creation. In addition, LLMs can be used to help programmers and developers write code. With it, they can write functions upon request, help users review code to identify and solve faults or give some code as a starting point. They can also use it to finish writing a program or command.

Generative AI (GenAI) is a broader AI category that aims to produce original content across multiple media, including text, images and music. It encompasses LLMs and extends to models like DALL-E for image creation and Jukedeck for music generation. While the goal is original content, because GenAI sources information from existing data sets, outputs may include verbatim lifts from sources or fabricated responses, known as hallucinations.

Retrieval-Augmented Generation combines generative AI with contextually relevant data to improve accuracy and reliability. It reduces AI "hallucinations" by grounding responses in a structured knowledge graph and validating them against a comprehensive knowledge model. Key components include contextual data enrichment, knowledge graphs, prompt enhancement and response validation.

RAG improves accuracy, speed, cost-efficiency, scalability and security in AI applications, making it ideal for customer service, knowledge management and research and development. The Progress Data Platform supports RAG by integrating various data sources, harmonizing, classifying and contextualizing the data and maintaining high data quality and governance.

The "Human-in-the-Loop" (HITL) concept integrates human oversight to provide accurate, reliable and repeatable data classification and AI outcomes. This approach leverages human judgment to review and validate AI outputs, improving quality control and addressing complex scenarios that AI alone might struggle to address. HITL is vital to many enterprise environments, where human intervention can prevent errors, promote governance and align decisions with societal values. By combining the strengths of both humans and machines, HITL creates a more adaptable, trustworthy and effective AI system, enabling technology to serve human needs responsibly and efficiently.

On the development side, we have robust pipelines and coding standards that have been refined in some cases over decades, and we have a human in the loop to review all changes. It’s important we use this opportunity to raise, not lower, our standards in terms of code quality and completeness, and in some cases, AI allows us to further shift left here with improved security and code reviews.

Speak to one of our AI experts and describe the opportunity at hand. Our team will identify the best path forward with our suite of products to help you achieve your goals.

Gain Transformational Efficiency: From GenAI content creation and personalization to automated AI-powered threat detection, AI delivers timesaving efficiencies at a scale and magnitude not previously possible.

Leverage Hallucination-free Business-relevant GenAI: Combine Large Language Models (LLMs) with semantic search of your own business data to generate GenAI outputs uniquely relevant to your business, with source attribution to engender trust.

Adopt Responsible AI Practices: Take a human-in-the-loop (HITL) AI approach to reduce data bias and inaccuracies while producing clear, context-driven outcomes without sacrificing efficiency.

Reinvent Customer Experiences: AI propensity scoring empowers you to harness digital channels to personalize and reimagine customer interactions, gaining both agility and competitive advantage.

Progress works to keep up with the pace of change in AI by adopting an AI-first approach—enabling humans in an AI-driven world to think and act differently. Instead of automative existing workflows, we are using AI to reimagine what is possible and in turn, changing how we operate. This enables people to address the conceptual strategic initiatives after the mundane tasks are accounted for. AI is enabling innovation across industries and domains so that what was hard is now easy and what was impossible yesterday is now probable tomorrow—and more things are possible every day. It’s an exciting time for all of us here at Progress, and it is great to see us at the heart of this innovation with so many of our customers.