01Operating platform
ArborPlus
An internal operating platform connecting sales, scheduling, production, reporting, customer communication, and field-service workflows at A Plus Tree.
Read the case noteOperations & Technology Leader
I build the operating systems leadership teams run on.
I turn production, margin, capacity, sales, and workflow problems into software, data systems, AI automation, and decision tools that work in the real world.
Charlotte, NC Open to the right next chapter
Current seatVP, Business Intelligence & Operations Technology
Built forService businesses with real operational complexity
Sweet spotWhere operating judgment meets product and data
Selected systems
Built close to the work.
The through-line is simple: find a consequential operating problem, make it legible, and build the system that lets people run it better.
02AI work system
Loomfield
A self-hosted mission-control system that lets different AI agents work from the same durable context, project structure, and human-controlled rules.
Read the case note Visit loomfield.ai — private beta waitlist open03Decision systems
Operations intelligence
Forecasting, capacity, backlog, pricing, margin, and production systems built around the questions leaders actually need answered.
Read the case noteHow I work
Software is a mechanism. The outcome is the job.
I work across the line that usually separates the operator from the builder. That makes it possible to carry a problem from leadership conversation to live system without losing the business context in between.
- 01
Find the real constraint
Start with the operating room, the field handoff, or the decision that keeps breaking—not with a technology shopping list.
- 02
Make the system legible
Define the workflow, metric, ownership, and source of truth so the organization can see the same problem clearly.
- 03
Build the missing layer
Use software, data, automation, or AI where it creates leverage. Keep the solution close to the work.
- 04
Run it until it holds
Ship, observe, correct, and turn the result into a durable operating habit rather than a one-time analysis.
Field notes
Ideas from the work.
Writing about operations, product systems, practical AI, and what I am learning while building Loomfield.
Preparing Loomfield for the Public
What changes when a private-beta AI work system has to become clear, trustworthy, and useful without its builder in the room.
Small Context, Serious Work
How Loomfield uses focused retrieval, smart micro-sessions, and the smallest capable model to spend context where it actually improves the work.
On the Desk: Teaching the Vault to Run the Work
A June workbench update on making the vault leaner, better routed, and responsible for carrying useful results into the next session.
Building Loomfield: an operating system for AI work
Why the next useful layer for AI is not a better chat window, but a shared brain, durable work, and controls that earn trust over time.
From Helpful Chats to an AI Operating System
What the first real limits of a shared AI vault reveal about rules, enforcement, coordination, and the missing operating layer.
Designing a Vault for AI Work
Early notes on building a durable Markdown home for projects, decisions, reusable instructions, and the context an AI assistant should carry forward.
On the desk
Next notes: why dashboards fail, building software from operating questions, and what AI systems need before they deserve automation.
Visit the writing desk