What did the JMR rebuild deliver?

16 departments rebuilt function by function, 14 live Airtable bases running in production, 21 SaaS subscriptions retired, 25+ AI agents in production, and $0 ongoing license cost. The business owns the result outright rather than renting it.

How big is the business in this case study?

A roughly 200 employee owner-operated logistics company. It is the company we run day to day on the systems we built, which is why the detail goes department by department rather than staying at the summary level.

JMR: 16 departments rebuilt into one operating system the business owns

How we rebuilt a ~200-employee owner-operated business into one owned operating system — department by department — replacing the rented SaaS stack with tools the business owns outright.

What the rebuild delivered

16 departments rebuilt function by function
14 live Airtable bases running in production
21 SaaS subscriptions retired
25+ AI agents in production
$0 ongoing license cost — owned, not rented

What this means for you

The same method applies to any owner-operated business: audit the real workflow across departments, close the gaps with tools you own, add AI where it earns its place, and train the team so the system sticks. No fabricated metrics — where a system is validated in dry-run rather than live, it is framed honestly as such.

Frequently asked questions

What did the JMR rebuild deliver?: 16 departments rebuilt function by function, 14 live Airtable bases running in production, 21 SaaS subscriptions retired, 25+ AI agents in production, and $0 ongoing license cost. The business owns the result outright rather than renting it.
How big is the business in this case study?: A roughly 200 employee owner-operated logistics company. It is the company we run day to day on the systems we built, which is why the detail goes department by department rather than staying at the summary level.
Does this only work for logistics?: No. The same method applies to any owner-operated business: audit the real workflow across departments, close the gaps with tools you own, add AI where it earns its place, and train the team so the system sticks.
Are the numbers in this case study verified?: Every figure comes from the live build, and there are no fabricated metrics. Where a system is validated in dry-run rather than running live, it is labelled that way instead of being counted as production.