Where the Four Machines Fit Best
A plain-words proposal: what each machine fixes, who it fits best, and how the same machine is sized down for smaller businesses.
Cold-Chain demo · Prior-Auth demo · Legacy Translator demo · Predictive Maintenance demo
Honest notes up front: These are demo machines with fake data only. No company named here is a client, and none has been contacted. No price is set — G sets every price. The pitch is never “AI.” It is always the specific fix and the money it saves, plugged into what the business already runs day to day.
At a glance
| Machine | Best fit, biggest first | Sized-down fit |
|---|
| Cold-Chain Logistics Agent | Medicine and food distributors | Regional food distributors, grocery suppliers, restaurant suppliers |
| Healthcare Prior-Authorization Pipeline | Health plans and insurers | Regional plans, clinic groups, specialty practices |
| Enterprise Legacy Data Translator | Big companies with many old systems | Any business stuck on old software |
| Industrial Predictive Maintenance Platform | Rail yards, refineries, big factories | Small factories, fleets, repair shops |
1. Cold-Chain Logistics Agent
Keeps cold shipments cold — and catches the bad one before it spoils
The leak it fixes: a load of medicine or food warms up on the road and nobody knows until it arrives spoiled. That is a lost load, a missed delivery, and a customer who stops trusting you.
Where it fits best: pharmaceutical distributors and large food distributors — the kind of operation Google named in its research (McKesson and Cencora are the big examples of this type). They move thousands of cold loads, so one saved load pays for a lot.
Sized down: a regional food distributor, a grocery supplier, or a restaurant supplier with a handful of reefer trucks. Same idea, smaller map: watch the truck, flag the warming load, reroute it through a cold depot or consolidate it before it goes bad.
Working demo: cold-chain-logistics-agent/index.html — fake shipments, fake temperatures, “Advance one hour” moves loads from SAFE to WATCH to AT RISK.
2. Healthcare Prior-Authorization Pipeline
Clears treatment approvals faster — with a person checking the hard cases
The leak it fixes: approvals sit for days while patients wait and staff chase paperwork by phone. Every day of delay is staff time burned and care delayed.
Where it fits best: health plans, insurers, and pharmacy benefit managers — the kind of operation Google named (UnitedHealth Group and CVS Health are the big examples of this type). They process approvals by the thousands every week.
Sized down: a regional health plan, a clinic group, or a specialty practice. Same rules check, smaller pile — and the hard cases always go to a human. This demo does not approve real claims and does not claim any compliance approval.
Working demo: healthcare-prior-auth-pipeline/index.html — three made-up cases checked against made-up policy rules.
3. Enterprise Legacy Data Translator
Turns old, messy records into clean, searchable records
The leak it fixes: the business runs on software from twenty years ago. Finding one old record takes a phone call and a hunt. New tools cannot read the old data, so staff retype it — or give up.
Where it fits best: big companies made of many older businesses — the kind of operation Google named (Berkshire Hathaway's mix of insurance, rail, and manufacturing is the big example of this type). Each subsidiary has its own old system, and none of them talk to each other.
Sized down: any business stuck on old software — a distributor, an insurer's local office, a manufacturer, a hospital billing office, a shop still running on flat files and spreadsheets. If the records are messy and hard to search, this machine fits.
Working demo: enterprise-legacy-data-translator/index.html — made-up flat-file lines turned into a clean, searchable table.
4. Industrial Predictive Maintenance Platform
Fix the machine before it breaks — not after it stops the line
The leak it fixes: a press, a truck, or a pump breaks without warning. The line stops, the repair costs more, and the downtime costs the most of all.
Where it fits best: rail yards and refineries and big factories — the kind of operation Google named (BNSF Railway and ExxonMobil refineries are the big examples of this type). One unplanned stop there is enormously expensive.
Sized down: a small factory, a fleet shop, a warehouse, a repair garage. Same sensors-and-warning idea, fewer machines: vibration, sound, and temperature readings flag the one machine that needs attention, and a work order is written before it fails. This demo makes no real defect-detection accuracy claim.
Working demo: industrial-predictive-maintenance/index.html — fake readings for four fake machines; “Check all machines” flags Press Line 1 and writes fake work order WO-2026-1047.
Prices (made 2026-10-26, month-to-month, cancel anytime)
| Machine | Setup | Small | Growth | Enterprise from |
|---|
| Cold-Chain | $527 | $327/mo | $747/mo | $1,597/mo |
| Prior-Auth | $527 | $299/mo | $549/mo | $1,597/mo |
| Legacy Translator | $2,097/project | $327/mo | $747/mo | $1,597/mo |
| Predictive Maintenance | $527 | $327/mo flat | $747/mo flat | $1,597/mo |
Full competitor comparison is in pricing-and-competitors-2026-10-26.md. Sensor hardware is separate. Healthcare does not approve real claims and makes no compliance claim. No business has been contacted.
How to use this
1. Show the demo for the machine that matches the business's leak. 2. Talk about the fix and the money saved, not the technology. 3. G sets the price and picks the target — nothing is quoted and nobody is contacted from this page alone.
All four machines are original builds with fake demo data only. No named company above has been contacted or is a client.