Michael Marolda
Product Marketing Manager, Senior
Michael Marolda is a seasoned product marketer with deep expertise in data, analytics and AI-driven solutions. He is currently the lead product marketer for the Progress Agentic RAG solution. Previously, he held product marketing roles at Qlik, Starburst Data and Tellius, where he helped craft compelling narratives across analytics, data management and business intelligence product areas. Michael specializes in translating advanced technology concepts into clear, practical business terms, such as Large Language Models (LLMs), Retrieval-Augmented Generation (RAG) and modern data platforms.
Articles by the Author
From Search to Answers: Rethinking Search in the Age of AI
Traditional search returns links. AI search delivers answers. Learn how organizations are transforming site search into a smarter knowledge experience.
How AI Video Indexing Improves Accuracy and Trust in Enterprise Search
Video has become a critical tool for troubleshooting, learning and getting work done. Learn how AI video indexing in Progress Agentic RAG contributes to enterprise AI strategy by unlocking knowledge stored in recordings, providing verifiable evidence for decisions, improving cross-team knowledge sharing and supporting governance.
How You Can Use RAG to Make Your AI Strategy More Cost-Efficient
This blog explores how RAG enables more efficient, governable AI, and why it’s now a critical requirement for scaling enterprise AI cost-effectively.
Progress Agentic RAG vs. Traditional RAG vs. ChatGPT: Choosing the Right AI Approach for Your Business
This post compares ChatGPT to traditional retrieval-augmented generation (RAG) and the Progress Agentic RAG solution, highlighting the difference between AI tools built for personal use and those designed to serve as governed knowledge layers across an organization.
Why Retrieval is the Real Engine of Enterprise AI
Retrieval is the foundation that determines what AI can reason over and what it can’t. This resource explains why retrieval strategy, not model choice, is the key to reducing hallucinations, preserving context, and delivering trustworthy enterprise AI answers.
What Is Agentic RAG?
Discover how agentic RAG combines Retrieval-Augmented Generation with autonomous AI agents to deliver accurate, context-aware and trustworthy responses. Learn why this advanced architecture outperforms traditional RAG for enterprise AI by facilitating dynamic workflows, compliance and efficiency.
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