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Package Description:
This coaching package is designed to help you go beyond quick demos and build a real MVP. By the end, you’ll understand how to scope, structure, and develop a working product you can show to users, teammates, or investors. Package topics can include, but are not limited to: • MVP planning: define the scope, features, and value proposition • Tool mastery: learn modern prototyping stacks and AI copilots in depth • Guided build: develop your MVP (web app, chatbot, or tool) • Testing: deploy in a sandbox and gather real feedback • Documentation: learn how to capture your process for iteration • Next steps: roadmap to refine, scale, and prepare for production Outcome: By the end of this package, you’ll have a working MVP in a testable environment—plus the documentation, skills, and confidence to keep developing.
What you'll get from this package
A working MVP (web app, chatbot, or tool)
Deployment in a testable sandbox/demo environment
Documentation of workflows and design decisions
A roadmap for moving from MVP to production
Additional details
Coaching delivered via live sessions.
Services included:
AI Fundamentals
Prompt Engineering
Talk to a team member
Schedule a call with a Leland team member who can help you explore your options.
Schedule a call
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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.
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