AI Insights

At Progress, we are taking a responsible and agile approach to Artificial Intelligence. We implement AI by first establishing clear processes to assess the suitability and performance of AI models and applications, as well as understanding data flows and ensuring appropriate protections—both within the models and across third-party integrations. We maintain compliance with industry standards by rigorously vetting all AI usage against our well-established company policies and requiring strict adherence to our AI Policy. 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.

An LLM is the engine. GenAI is what you build with it. Neither produces grounded, defensible output on its own, that's the job of the context layer underneath. Without enterprise data, semantic meaning, and policy controls, an LLM generates plausible text. With them, it generates answers you can defend in production. That context layer, control and agility is what Progress provides.

RAG grounds an LLM in your own data by retrieving relevant content before generating an answer. Most implementations use vector search, which matches on similarity but not on meaning. Progress adds semantic enrichment, graph context, and policy controls to the retrieval step, so the model gets the right answer, not just the closest one. That's the difference between a chatbot and a production AI system.

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.

Governed AI isn't a trade-off against innovation, it's the precondition for scaling AI in regulated industries. Every output should be traceable to its source, every decision tied to an explicit rule, every agent action bounded by policy. That's how AI gets through procurement, compliance, and audit - and stays deployed. Progress software is built on that principle, which is why it powers AI in in some of the most regulated industries and use cases.

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.

The models change every quarter. The context layer underneath them shouldn't. Progress software is designed as durable infrastructure; so when a new LLM, agent framework, or retrieval technique emerges, you swap it in without rebuilding your semantic graph, your governance rules, or your data fabric. That's what responsible AI looks like: the headlines change, your stack doesn't.