What actually got built.

The problem

A one-truck shop runs out of one overloaded head. Which filter fits this unit, how much refrigerant that system holds, the gate code, what's due this week — it all lives in a spreadsheet nobody opens on the way out the door.

The big fix

A pre-job brief, written from the shop's own equipment sheet: the filter first, the refrigerant amount, with the EPA tracking flag only when it crosses the 15-lb line, the access notes, the due date. Before every job, plus a morning rundown of the week.

Here's the workflow

1. Zapier posts the job's row
2. he replies with the brief in plain English
3. a scheduled Zap writes the morning rundown, soonest job first

What you get · A brief before every job plus the morning rundown of the week

Built overnight against a fictional demo shop — the origin prototype every later trade copies, not a live client.

“Mr. Wilson started as a spreadsheet, a Zapier flow, and one rule: tell the tech what they need before the job, and never make up what you don't know.”

The problem

Every claim that hit the mailbox got opened, read, and re-keyed by hand before a decision could even start. Fraud signals — doctored receipts, edited photos — were caught by eye, if at all.

The big fix

A pipeline that clears the claims mailbox: pulls the claim number, screens the attachments, routes by risk, and files everything into the carrier's claims system. People only touch what needs judgment.

Here's the workflow

1. extract the claim number — pattern-match first, AI only when unsure
2. screen attachments for fraud signals
3. route by risk (AI fraud-risk score is concept — today's screening is rule-based, and we say so)

What you get · Low risk filed automatically; everything else flagged for a person

A proof of concept, tested against mock data, not yet wired to the live carrier.

“The fastest AI win in most businesses isn't a chatbot — it's deleting the paperwork nobody should be doing by hand.”

The problem

The team's daily experience of AI was headlines and hype. Recognition, meanwhile, took somebody hours per person, so mostly it didn't happen.

The big fix

A no-code node workflow: one photo and a one-line note in, a personalized celebration film styled to that person's favorite show, plus a branded award, posted straight to the team's channel.

Here's the workflow

1. read the note, pick the theme
2. cast the person into the scene
3. lock a real film look
4. animate and score it, then build the certificate and post

What you get · A celebration film styled to their favorite show + a branded award

Real and running, week after week. No engagement numbers quoted — the honest proof is people asking "who's making these?"

“The best way to get a team excited about AI isn't a training deck. It's making them the star of something delightful.”

The problem

A grieving family has a shoebox of photos, some home video, and a song — and nothing between a PowerPoint slideshow and hiring a creative agency. When my own mother passed, that gap was personal.

The big fix

An engine, ruby-v1, named for my grandmother, that reads everything the family brings, derives one custom look for that one person, and renders a beat-synced tribute film plus the matching keepsakes: prayer cards, programs, signage.

Here's the workflow

1. derive the family's look and voice
2. caption every photo
3. mine the home video for the moments that matter
4. cut to the song's beats, era-graded

What you get · A tribute film scored to their song + matching keepsake set

The engine is real and has made tribute films for real families, mine first. Pre-revenue, in pilots with funeral homes — I'd rather say that plainly than invent traction.

“The point of AI here isn't speed. It's care at scale — the kind of memorial that used to take a creative agency.”

The problem

A 125-year-old church run by volunteers. Every Friday someone re-typed the bulletin into the website by hand. At-home viewers couldn't follow the song lyrics on the stream. And 125 years of records sat fading in a basement.

The big fix

One five-minute upload: drop in Friday's bulletin PDF and the site refreshes itself — services, songs, scripture, a fresh header image. A live-lyrics overlay for Sunday's stream and a searchable 125-year history portal round it out.

Here's the workflow

1. AI reads it into clean data
2. the site republishes itself
3. the lyric overlay arms from the songbook files the team already keeps

What you get · The site, current for Sunday — republished automatically

The Friday upload works end to end. The live lyric sync is built but partial, still running demo lyrics. The history portal (75 searchable events) is the most finished piece.

“AI doesn't have to replace the people who make a place special. Sometimes it just gives them their Friday back.”

The problem

A volunteer coach runs a whole team out of a roster spreadsheet, parent group texts, and a game-plan notebook. A real tool to replace that pile used to take a development team and a budget.

The big fix

A complete coaching tool — brand, feature set, product spec, clickable prototype — built solo with AI, no code written by hand. Underneath it, a real algorithm that generates fair lineups and playing time.

Here's the workflow

1. Describe it
2. AI builds it
3. Refine

What you get · A clickable coaching prototype: roster, schedule, comms, playing-time planner

A designed, working prototype — no users yet. The lineup engine is a real deterministic algorithm, not yet wired into the UI; the in-demo "AI game plan" is a stand-in.

“One person described the tool he needed, and it exists. That's the whole reason a done-for-you service can exist for the shop down the street.”