Many enterprises are excited about generative AI, but turning that excitement into scalable results often proves difficult. The challenge is that most enterprise data is complex, interconnected, and highly contextual. Traditional large language models can generate fluent text, but they don’t truly understand the domain.
On February 11, we invite you to join a webinar hosted together with our partners at Tietoevry, where we’ll explore how Knowledge Graph-driven Retrieval-Augmented Generation (RAG) helps AI move from guessing to genuine understanding.
Mohammad Shadab, Senior Data Architect, and Sebastian Remnerud, Senior Solution Consultant at Tietoevry, will walk through two real customer cases that show how this approach works in practice:
Case 1: Turning IoT data into meaningful insights
Sensor data alone rarely tells the full story. By combining knowledge graphs, LLMs and enriching IoT data with external sources such as weather, organizations connect previously unrelated signals and move beyond basic monitoring to enable predictive maintenance and profitability insights.
The result: IoT data that finally tells a clear story, enabling predictive actions and confident decisions.
Case 2: Smarter recipes with domain-aware AI
By grounding generative AI in structured domain knowledge, this solution significantly improves content quality, dietary constraint detection, and personalized recipe recommendations, enabling intelligent recipe transformation while preserving nutritional intent and culinary context.
The result: Now users can trust AI because it understands the domain, not just the words on the page.
Whether you’re working with enterprise data, AI initiatives, or IoT systems, this session will offer practical insights you can apply right away! Along with time to raise your own questions and challenges.
