Many AI projects are often impressive solutions that struggle to find a meaningful problem to address, and those in the boardroom recognize this reality. To survive the transition from prototype to production, AI must move beyond the chat window and into the workflow. In this talk, we’ll explore the technical and strategic foundations required to build AI that actually sticks.

We will dive deep into the engineering "gotchas," including:

The Model Selection: How to find the "ground truth" on LLM performance and reasoning capabilities before you commit to a provider.
Vector Store Reality Check: Navigating the hidden challenges of indexing, metadata filtering, and dimensionality mismatches.
The RAG Challenges: Explore RAG latency challenges.
Azure DocumentDB: Introduction to a new open-source NoSQL database compatible with MongoDB that offers vector search capabilities and can operate on-premises.
The Human-in-the-Loop: Why human intervention is a non-negotiable technical requirement and how to architect systems that augment expert decision-making.