AI Engineering: Build & Deploy Real-World RAG

Stop building "hello world" wrappers and start building production-ready AI systems.

Moving an AI project from a prompt to a scalable, secure implementation is a massive technical hurdle. For $30, I’m sharing the technical blueprints and architectural lessons I’ve gathered from building complex AI systems—including RAG pipelines and on-premise neural integrations.

In this 1:1 session, we can dive deep into the technical "how-to" of modern AI engineering.

What we can cover:

• RAG Architecture: Optimizing retrieval, vector database selection, and handling data privacy in local RAG pipelines.
• The Model Context Protocol (MCP): How to use MCP to give your AI models better access to your local data and tools.
• On-Premise vs. Cloud: Navigating the trade-offs of deploying neural voices and LLMs locally for security or performance.
• System Design: Designing AI-driven support systems and telecom-grade interfaces that actually work in high-stakes environments.

This session is ideal for:

• Software Engineers looking to pivot into AI Engineering.
• Technical Founders building AI-first products.
• Architects tasked with implementing secure, local-first AI solutions.

The Goal: You’ll walk away with a technical reality check on your architecture and a clear path to deployment, skipping the common errors that plague first-time AI implementations.

Let’s build something that scales.