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Description: This coaching package is designed to give you a clear, practical introduction to AI productivity. By the end, you’ll understand where AI can save you time, have hands-on experience with workflows, and know exactly how to keep building them into your routine. Package topics can include, but are not limited to: • Use case exploration: identify your top productivity opportunities • Tool setup: experiment with AI in Notion, Slack, Gmail, and more • Prompting basics: learn to structure effective prompts for daily tasks • Guided build: create 1–2 custom workflows together • Workflow design: learn iteration and optimization techniques • Next steps: resources and roadmap to continue expanding Outcome: By the end of this package, you’ll have a foundation in AI-powered productivity and at least one working workflow running in your stack.
1–2 custom AI workflows tailored to your needs
A starter prompt library for everyday tasks
Hands-on practice in productivity tools (Notion/Slack/Gmail)
A roadmap for further automation
Coaching delivered via live sessions.
Services included:
AI Fundamentals
AI Research
Schedule a call with a Leland team member who can help you explore your options.
Schedule a call
Get help with AI Ethics & Responsible AI, AI Research, and .

Joined November 2025
5.0
Production AI Agents at Live Nation | Databricks, MCP, & Vertex Expert
I build AI agents for real analytics environments - not demos, not proofs of concept. As Director of Business Intelligence and Analytics at Live Nation Entertainment, I've designed and shipped production agent systems that combine LLM reasoning, MCP servers, structured data querying, and document search into workflows that replace manual analytical processes. My hands-on stack includes Databricks, Gemini, Vertex AI Search, Streamlit, and custom MCP server development. I've also built open-source tooling in this space, including a video understanding MCP server using local/free models. I come from a decade-plus background in BI and data engineering, which means I understand the messy reality agents actually have to operate in - legacy data models, inconsistent schemas, stakeholder trust issues, and the gap between what LLMs promise and what they deliver in production. If you're trying to build your first agent, figure out the right architecture for your use case, or get an agent system actually adopted inside your organization - I can help you avoid the mistakes I've already made.
5h of coaching