Applied AI

AI that runs your operations.
Not a chatbot that forgets.

Most people use AI as a smarter search box — stateless, single-turn, gone when the tab closes. I build the opposite: AI as a coordinator sitting on a durable, structured system, so the work compounds instead of evaporating. Every run leaves a real artifact behind.

Agentic, not a toy

Systems that act, remember, and coordinate real work — reconciliation, data pipelines, reporting — end to end.

Trustworthy with numbers

LLMs hallucinate figures, so mine never compute one. Code does the math; the AI does the writing; a check validates every claim before send.

Yours to keep

Maintainable, self-contained tools a non-technical operator runs unattended. File-based beats service-based. No dependency on me.

See it work

The follow-up that collects while you sleep

A reminder bot running on one overdue invoice. Press play and watch the sequence chase it from issued to paid — no one remembering to send a single email, so your team spends its time replying to customers and applying payments instead of chasing them.

Invoice sent
Day 7
Day 14
Day 21
Paid
IssuedInvoice #1042 · $6,200 · net-30 issued to Acme Co.
Day 7Auto-reminder emailed — a friendly heads-up, a week before it’s due. Payment link attached.
Day 14Second nudge on the due date: “Invoice #1042 is due today,” one-click pay attached.
Day 21
  • ⚠ Now 7 days past due — escalation notice sent automatically
  • → flagged for a personal call, so nothing slips silently
Paid✓ Paid — cash in the bank. Across the ledger: $18,400 collected · average days-to-pay 34 → 11 · 0 manual follow-ups.
Every invoice gets chased on schedule — without anyone remembering to.

This runs unattended on your receivables. Want your invoices chasing themselves? Book a free 20-min call →

See it work

The guardrail that protects your books

This is the pattern behind the tools I ship. Press play and watch the system reconcile a batch of deposits — then catch a duplicate that would have double-counted revenue and quietly thrown off the books.

Ingest
Reconcile
Draft
Verify
Post
Ingest3 CSV exports · 128 deposits · $22,940 total
Reconcilecode totals every row & checks each deposit ID against the posted ledger
AI drafts“128 deposits totaling $22,940 are reconciled and ready to post to the books.”
Verify
  • ✓ 122 deposits balanced and new to the ledger
  • ⚠ 6 deposits ($4,200) were already posted last Tuesday — duplicates
  • → held back: posting them would double-count revenue and break the books
Post✓ Posted 122 net-new deposits ($18,740). Zero double-entries — the books stay correct, and the $4,200 error never happened.
Nothing posts until it’s reconciled and de-duplicated.

This is the actual safeguard logic I build. Want it running on your books? Book a free 20-min call →

The method

How an idea becomes a system your team keeps.

01

Constrain

Find the one bottleneck actually limiting throughput — with Theory of Constraints, not guesswork. AI effort goes there and nowhere else.

02

Build

Ship a working tool, not a recommendation. Agentic where it earns its keep; a plain script where that’s smarter. It has to run live.

03

Verify

Code computes every figure; the AI only writes the words around it. Each number is sourced and machine-checked before anything ships.

04

Hand off

Self-contained and documented, so a non-technical operator runs it alone — no developer, no service to babysit, no me required.

What I build

Live systems, not slideware.

Flagship

File-based accounting converter

Turns a nonprofit’s messy CSV exports into clean, import-ready accounting files — one click, offline, with a guardrail that makes double-imports impossible. Run daily by a non-technical admin.

Automation

Reconciliation engines

Automated matching — bank reconciliation, category matching — that clears the work a person used to grind through by hand.

Agents

Reminder & follow-up bots

Unattended agents that chase overdue items and nudge stakeholders on schedule, without anyone remembering to.

Analytics

Forecasting & segmentation

Revenue forecasting and customer segmentation workflows that put decisions on current numbers, not stale spreadsheets.

Method

Capability analysis

Control charts that reveal whether you’re missing targets from variance you can tighten — or because the process can’t reach the goal and needs a step-change.

Scale

Self-serve AI onboarding

Each person’s own AI runs a structured interview to gather requirements; the output feeds straight into config. Scales to 50 the same as 5.

Proof I practice what I preach

I run GO-EZ itself on an agentic AI system.

This practice — the morning briefings, client-engagement tracking, the knowledge base, the branded deliverables — runs on a custom agentic AI operating system I built. It has real guardrails: a read / write / destructive permission model so it never touches a client’s books or inbox without my explicit approval. It’s the same discipline I bring to your operations: powerful automation, with the safety rails that make it trustworthy.

Put it to work on your operation