Schedule a call with a Leland team member who can help you explore your options.
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This coaching package is designed to help you master AI for productivity. By the end, you’ll have a personalized toolkit of workflows and automations running across your daily tools, plus the knowledge to keep expanding them. Package topics can include, but are not limited to: • Workflow mapping: identify bottlenecks and automation opportunities • Tool mastery: advanced features of productivity platforms • Prompt libraries: build and test reusable prompt sets • Guided build: set up 3+ automations across tools • Integration: connect workflows into an end-to-end system • Next steps: documentation and roadmap for scaling further Outcome: By the end, you’ll have a customized AI productivity toolkit with multiple automations actively running—plus the confidence to keep scaling efficiency.
A customized AI productivity toolkit
At least 3 automations actively running
A reusable prompt library
Documentation for workflows and future scaling
Coaching delivered via live sessions.
Services included:
Prompt Engineering
Schedule a call with a Leland team member who can help you explore your options.
Schedule a call
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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.
10h of coaching