How to design and setup a Lakehouse architecture using Azure Synapse/Databricks

The rise of Large Language Models (LLMs) like ChatGPT and Google Gemini is transforming how businesses operate. From accelerating content creation to analyzing complex datasets, the possibilities seem endless. But as we embrace these powerful tools, a crucial question arises: what exactly happens to the sensitive company information you input into these tools?

AI agents are the next step in artificial intelligence, moving beyond generative AI into systems that act, decide, and adapt. Businesses are now exploring how AI agents create real value, but adoption comes with risks and challenges.

With a pilot launch planned in just one month, our client wanted to ensure a Q&A chatbot was safe, compliant, and reliable. Enter our AI Risk Assessment Workshop, a collaborative session led by our Responsible AI experts to identify and mitigate risks. In this post, we’ll walk you through how we addressed the client's challenges and why this process is critical for responsible AI deployment.

Your data isn’t lacking infrastructure. It’s lacking impact.
Product thinking brings the focus back to business outcomes—by treating data like a product, not a project.