The problem
AI can speed up research, summarization, document review, workflow drafting, and follow-up preparation, but advisor contexts require accuracy, privacy, review, and judgment.
The risk is not just that an AI output is wrong. The risk is that it sounds confident, hides uncertainty, drops source context, or makes the human reviewer work harder to understand what is supported.
What I have been testing
- Structured prompts that define the task, source boundary, audience, and output format.
- Review loops that separate draft generation from human approval.
- Source citations, uncertainty flags, and evidence summaries.
- Document extraction patterns for tax, insurance, real estate, and planning evidence.
- Workflow checklists that make follow-up, missing items, and assumptions visible.
My role
- Workflow design and prompt architecture.
- Advisor-operations translation from task to reviewable system.
- Risk-aware implementation for source handling, uncertainty, and approval steps.
- Testing practical patterns across document-heavy planning workflows.
Screenshots or artifacts
Prompt pattern
Inputs, source boundary, reviewer role, output format, and uncertainty flags made explicit.
Review checklist
Supported facts, assumptions, missing evidence, and required human decisions separated clearly.
Workflow diagram
Document intake to extraction, draft, audit, reviewer notes, and approval.
Example output
A sanitized review artifact with source notes and uncertainty visible to the reviewer.
What it demonstrates
- Practical AI implementation beyond demos and generic chat prompts.
- Risk awareness for advisor workflows with sensitive documents and professional judgment.
- Ability to design human-in-the-loop systems that preserve accountability.
- Operational thinking across extraction, review, documentation, and follow-up.
- Clear translation from AI capability to usable business process.
Discuss similar work
If you are trying to bring AI into document review, research workflows, advisory operations, or internal quality-control systems, this is the implementation layer I like working on.