Why a Heron?

The heron is my personal and professional mark.

Herons are patient observers. They can remain remarkably still while reading their environment, then move with speed and precision when the moment is right.

They live at the intersection of stillness and action — watching carefully, finding the signal, and acting deliberately.

That combination has become a reminder of how I try to work:

Observe deeply. Find clarity. Move decisively.

Matt McGreal
Founder & AI Workflow Architect · AtlasFlow

Matt McGreal

I turn messy operations into clear, AI-enabled workflows people can actually use.

I work with business owners and operators to understand how the business really works, model the decisions and handoffs, and build validated prototypes that surface risk, priorities, and next actions.

View selected systems →See how I work
Selected work

Workflow architecture brought to life.

Three working explorations that demonstrate operating-model design, data structure, AI workflow thinking, human review, rapid prototyping, and cross-platform automation.

01 / Real estate operations
Case study

Ada

Transaction intelligence for real estate operations

An AI-assisted operating environment that keeps the agent focused on the few moments where judgment, communication, or action can change the outcome.

Explore Ada ↗

Overview

Ada helps a real estate operator manage active transactions through a clear operating view. It turns scattered updates into structured visibility so she can quickly see what is moving, what is stuck, and what needs attention.

Challenge

Transactions generate constant noise across email, deadlines, documents, inspections, financing, and client communication. The problem is not collecting information. It is separating routine progress from the few issues that truly require intervention.

Solution

Ada applies one operating principle: interrupt only when judgment can materially change the outcome. The MVP combines custom intake, an Airtable data spine, AI structuring and drafting, and a review layer for edit, approval, and action.

Benefits

  • Action vs. awarenessKeeps routine progress in the background and elevates the few items that need intervention.
  • Status-first viewShows what is moving, what is stuck, and what the next action should be.
  • Human controlSupports review and judgment before communication or execution.
  • Lean MVP pathConnects intake, AI prep, review, and execution without overbuilding.
RoleAI workflow architect and prototype builder
StatusLean MVP in development
ValidationDesigned and refined with an experienced real estate operator
ToolsAirtable, OpenAI API, GitHub, Vercel, custom HTML
Ada AtlasFlow portfolio case study
02 / Plumbing service operations
Case study

Cal

Service-request triage for plumbing operations

An AI-assisted service workflow that helps a plumbing business capture requests cleanly, see urgency fast, and start the day with clarity.

Explore Cal ↗

Overview

Cal helps a plumbing business capture incoming service requests, structure the details, and surface what should happen next. It gives the owner a clearer operating picture without adding another layer of work.

Challenge

Service businesses juggle calls, emails, and urgent issues while routing jobs, protecting technician time, and responding quickly. The risk is inconsistency: weak intake, unclear priority, and too much mental sorting by the owner.

Solution

Cal uses reusable AtlasFlow methods to turn intake into structured triage. The prototype combines Airtable, AI classification, recommended next actions, a morning brief, and automated notifications so the team can move from request to response faster.

Benefits

  • Cleaner intakeStructures job type, urgency, and key context at the point of capture.
  • Faster triageFlags red, yellow, and green work so attention lands where it matters.
  • Better owner visibilityCreates a simple daily view of demand, risk, and next actions.
  • Reusable foundationShows how one steel thread can extend into broader service operations.
RoleAI workflow architect and prototype builder
StatusFunctional end-to-end prototype
ValidationDesigned and refined with an experienced plumbing business owner
ToolsAirtable, Zapier, OpenAI, custom HTML
Cal AtlasFlow portfolio case study
03 / Clinical education operations
Case study

Arc

Clinical operations intelligence for CRNA education

An operating console that turns messy schedule-change requests into structured, reviewable decisions for clinical education teams.

Explore Arc ↗

Overview

Arc helps clinical education teams see what changed, what is at risk, and what to do next. It organizes schedule-change requests into a clear operating view so staff can review context and decide with confidence.

Challenge

Clinical placement changes are messy. Requests arrive through forms, email, and text, with downstream effects on readiness, site coverage, and student progression. Teams need clarity without another dashboard or more manual coordination.

Solution

Arc converts schedule-change requests into structured, reviewable decisions. The MVP combines an Airtable data spine, AI readiness and risk assessment, and a human review interface that surfaces evidence, recommends next steps, and supports escalation when needed.

Benefits

  • Risk-based prioritySurfaces the requests most likely to affect readiness or coverage.
  • Decision-ready contextBrings change, impact, and the next action together in one place.
  • Human review preservedSupports coordinator judgment instead of automating the decision away.
  • Strong MVP focusAnchors the product around one steel thread with a credible VPOC path.
RoleAI workflow architect and prototype builder
StatusMVP defined; VPOC in development
ValidationDesigned and refined with an experienced clinical education leader
ToolsAirtable, n8n, OpenAI, Claude Code, Vercel API, custom HTML
Arc AtlasFlow portfolio case study
The AtlasFlow methodology

How I Build

From Signal to System

Every successful product begins with uncertainty. Rather than jumping directly into building, I move through four deliberate stages that create clarity, produce evidence, and inform every decision that follows.

Every stage answers a different question, produces a different kind of evidence, and gives the next stage a clearer starting point.
Great blue heron with reeds in a circular frame

Each stage informs the next, so I build what the evidence supports.

1. Understand

Product Thesis

We begin with a belief about what matters, who experiences the problem, and what a better outcome could look like.

Problem uncertainty
Question

What problem is worth solving?

Why

Building begins with a belief about what matters, who experiences the problem, and what a better outcome could look like. The thesis creates direction before effort.

Produces
  • Defined operational problem
  • Target user
  • Desired outcome
  • Initial value proposition
  • Core assumptions
  • Success criteria
Reduces

Problem uncertainty

Are we solving the right problem for the right person?

Informs next

The thesis identifies the most important operational workflow to model as the Steel Thread.

TransitionThe thesis defines what matters. The Steel Thread defines how to test it.
Portrait of Matt McGreal
About Matt

I understand the business before I design the system.

My background is in enterprise business development, discovery, relationship building, and complex solution design. AtlasFlow is where that experience becomes working AI-enabled operating systems.

I am most effective at the intersection of business and technology: listening closely, making complexity visible, modeling the workflow, and building enough of the system to prove what matters.

Operational discoveryWorkflow and data modelingHuman-centered AI designPrototype implementationExecutive communicationValidation and handoff
Contact

Let’s turn operational complexity into clarity.

I’m open to AI workflow architecture roles, operating-model design, and focused prototype engagements with teams that need clearer decisions, governed workflows, and practical next actions.

LinkedInRésumé
Based in Westfield, Indiana
AtlasFlow © 2026 · Matt McGreal