lisAIconsulting

Portfolio

Examples of practical AI, automation, and workflow design

These examples reflect the kind of work I design and deliver: structured intake, process improvement, and lightweight automation that solve real business problems without unnecessary complexity.

Selected work

Two examples are shown below: workflow automation and structured AI-assisted intake. Together they demonstrate how I bring clarity, structure, and execution to business processes.

Example 1

n8n Defect Intake & Tracking Automation

This workflow transforms a manual defect intake process into a consistent, trackable operating model. A structured form captures inputs, n8n routes the data, and results are recorded in a shared tracking system.

Business problem

Inconsistent defect reporting creates rework, slows triage, and reduces visibility.

Approach

Standardized intake and automated routing using n8n with a shared tracking output.

Value

Cleaner inputs, reduced manual handling, and improved visibility across teams.

What it demonstrates: translating a manual process into a structured, repeatable workflow.

Demo walkthrough

End-to-end flow from intake to tracking:

Defect intake form used to capture issue details.
Step 1 — Intake form: structured capture of defect details to ensure consistent inputs.
n8n workflow showing automation process.
Step 2 — Workflow automation: n8n processes and routes the intake into a structured flow.
Google Sheet output for tracking.
Step 3 — Shared tracking: submissions are recorded in a centralized sheet for visibility and follow-up.

Example 2

Business Intake & Prioritization GPT

This solution improves intake quality by guiding users through clarification before producing a structured, decision-ready summary.

Business problem

Requests are often vague or incomplete, leading to delays and rework.

Approach

AI-driven intake that asks questions first, then structures the request.

Value

Faster prioritization with clearer, more complete inputs.

What it demonstrates: AI used to improve process clarity—not just generate content.

Demo walkthrough

Transformation from vague request to structured output:

Initial messy user request before clarification.
Step 1 — Raw request: the interaction begins with an unstructured business request.
GPT asking clarifying questions.
Step 2 — Clarification: targeted questions gather missing business context.
User answers providing detail.
Step 3 — Context captured: responses provide the necessary detail for structured output.
Final structured output.
Step 4 — Structured output: the request is transformed into a clear, decision-ready summary.

How I approach this work

I focus on business-ready solutions: clarify the problem, define the workflow, reduce friction, and apply the right level of automation.

If your team needs help improving intake, workflow clarity, or execution consistency, I’d be glad to discuss it.