Profisee Modernizes MDM for the Agentic AI Era
This positions Profisee as a foundational layer for "agentic AI" in data estates, where multiple AI agents collaborate on complex tasks like supplier relationship analysis.
Profisee, a leader in modern Master Data Management (MDM) solutions, has introduced Aisey as its AI-driven, agentic assistant.
This launch represents a significant evolution in MDM by embedding artificial intelligence directly into data management workflows, making the process more intuitive, automated, and accessible.
Aisey is purpose-built to address longstanding challenges in MDM, such as manual configuration, ongoing data maintenance, and the expertise gap that slows down adoption.
By leveraging large language models (LLMs) and autonomous capabilities, Aisey transforms Profisee’s platform from a traditional MDM tool into an “AI-first” system that accelerates configuration, governance, and daily operations.
Empowering Microsoft AI Adoption
The introduction aligns with Profisee’s broader strategy to power AI initiatives across ecosystems like Microsoft Fabric, Azure, Databricks, and Snowflake. As organizations scale AI adoption, Aisey ensures master data remains trusted and “consumable” (deduplicated, standardized, validated, complete, and self-describing), reducing risks like AI hallucinations and enabling faster business outcomes.
Profisee’s Master Data Management (MDM) platform is natively integrated with Microsoft Fabric, Microsoft’s unified analytics platform, to deliver trusted, high-quality data directly within the Fabric ecosystem.
This integration positions Profisee as the first and only MDM solution embedded as a native workload in Fabric, enabling seamless data governance, stewardship, and preparation for AI, analytics, and business intelligence without requiring users to switch tools or ecosystems.
AI Readiness
Profisee’s AI Readiness solution leverages Master Data Management (MDM) to prepare organizations’ data for seamless AI integration across business processes. It addresses key challenges like siloed, inconsistent, or unreliable data that can lead to AI “hallucinations,” ensuring data is trusted, transparent, and consumable for AI tools such as copilots, dashboards, and analytics.
The goal is to deliver “gold medallion” data—reproducible, explainable, compliant, and efficient—while enforcing governance, quality standards, and metadata capture for better BI reporting and operational efficiencies.
