Danielle Shante'
AI Infrastructure Architect, Educator & Executive Producer
Atlanta, Georgia, United States
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Danielle Shante’ is an AI Infrastructure Architect, internationally certified AI consultant and facilitator, educator, and Founder and CEO of DSWM Enterprises LLC.
She is the creator of AURELIQ™, a 14-seat executive AI architecture designed around specialized responsibilities, coordinated workflows, and human oversight.
With approximately 17 years of experience in education, Danielle brings a practical, accessible approach to helping professionals understand and implement emerging technology.
She is also a two-time summit creator, executive producer, and showrunner, having directed the digital infrastructure, creative production, speaker coordination, scheduling, and execution of two business and technology summits in 2026.
Her work focuses on helping organizations move beyond isolated AI tools toward repeatable, accountable operational processes.
Area of Expertise
Topics
From Prompt to Process: Build and Test a Human-Governed AI Workflow
Most AI demonstrations end with an impressive answer. Real business operations require something more: reliable inputs, structured outputs, clear responsibilities, human oversight, and a way to measure whether the workflow actually works.
In this full-day, hands-on workshop, participants will build and test a human-governed AI workflow using a realistic, fictional business-request dataset.
Starting with sample data, attendees will design a structured intake process, develop AI instructions for extracting and classifying information, establish routing and escalation rules, introduce human approval checkpoints, and evaluate results against expected outcomes.
Participants will test their workflows using ambiguous requests, missing information, and common AI failure scenarios. They will then refine their instructions, improve decision boundaries, and measure the quality and consistency of their results.
The workshop emphasizes practical implementation rather than product demonstrations. Participants will leave with a functioning workflow prototype, reusable templates, a structured evaluation checklist, and an actionable plan for adapting the framework to their own organizations.
No advanced programming experience is required. Optional technical extensions will be available for attendees familiar with APIs and automation tools.
From Prompt to Process: Build and Test a Human-Governed AI Workflow
Most AI demonstrations end with an impressive answer. Real business operations require something more: reliable inputs, structured outputs, clear responsibilities, human oversight, and a way to measure whether the workflow actually works.
In this full-day, hands-on workshop, participants will build and test a human-governed AI workflow using a realistic, fictional business-request dataset.
Starting with sample data, attendees will design a structured intake process, develop AI instructions for extracting and classifying information, establish routing and escalation rules, introduce human approval checkpoints, and evaluate results against expected outcomes.
Participants will test their workflows using ambiguous requests, missing information, and common AI failure scenarios. They will then refine their instructions, improve decision boundaries, and measure the quality and consistency of their results.
The workshop emphasizes practical implementation rather than product demonstrations. Participants will leave with a functioning workflow prototype, reusable templates, a structured evaluation checklist, and an actionable plan for adapting the framework to their own organizations.
No advanced programming experience is required. Optional technical extensions will be available for attendees familiar with APIs and automation tools.
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