Case studies · receipts, not theory
Different businesses, different industries, the same root problem: systems that don’t talk and people doing by hand what a machine should handle. Here is what fixing it looked like, in real numbers, across seven of them.
The frame
The work looks different in a tax practice than it does in a trucking company, but underneath it is the same job every time: make systems that were never built to talk to each other work together, take the manual steps out, and decide where a machine should make the call instead of a person.
That is the thread running through all of these. Read them less as a brag sheet and more as a pattern — because the pattern is the point.
File 01
Accounting / Professional services
The anchor case — closest to home
A CPA firm’s most experienced people were buried in bookkeeping and tax prep — the low-value work that left no room for the thing clients actually pay a premium for: talking to them.
We extended the team beyond the US, stood up a sub-team to take over that load, automated the data flow between their systems, and removed the human decisions that bookkeeping and tax prep no longer need. We also built partnerships with other financial professionals so they could serve clients more completely — and open a new line of revenue.
0hours
given back to the US team, put into thousands more client meetings. A big jump in capacity, no extra office space, lower cost.
Files 02–07
Entered in the ledger · industry by industry
02
Staffing / Recruiting
Their tools did not talk, so the team burned hours copying data by hand — and others had tried to connect them and failed. We killed the manual copying, used AI to handle candidate selection where a human was not adding value, and pulled their scattered data into one place.
~0 hrs
of human labor and millions of dollars in payroll saved — annually
03
Ecommerce / Product launches
Most launches go out cold and hope demand shows up. For a set of ecommerce and Kickstarter clients, we built the opposite: a system that pulled prospects off Shopify and other platforms into a community before launch, so each one opened to a warm list of buyers.
~$2M in sales
across several clients — each launch landing 250K–650K inside a 60-day window
04
Logistics / Transportation
The operation lived or died on keeping new drivers coming in, with no reliable way to recruit, train, and spot the keepers. We built the processes, systems, and mobile apps to recruit and train drivers fast enough to stay ahead of the churn.
Shortage, ended.
No more delivery delays dragging down their delivery rating
05
Publishing / Authors & authorities
They had authority but no engine to turn attention into leads. We set up the team and the process to run a content strategy that delivered genuinely engaging social content.
Tens of thousands
of new leads generated
06
Creators / Online education
Content creators needed a way to attract leads and sell their content and community at scale. We built and launched the system that let them do it, using Facebook as the engine.
~0
leads generated
07
Education / Multi-location franchise
Enrollment and tuition were tracked by hand across every location, with a manual review behind every accounting report and licensing invoice. We built an enrollment system that tracks students and tuition across all of them — and feeds accounting and the fees owed to corporate.
Hundreds of hours
saved — previously lost to manual review
Seven files, one thread. The pattern is the point.
Seven industries, one underlying skill: making systems that were never built to talk to each other work together, taking out the manual steps, and deciding when a machine should make the call instead of a person. That range is the whole credential — and the reason your industry is not the exception.
Your industry isn’t special. I’ve fixed this in six others. Yours is next.
Where it starts
Your business’s Renaissance starts with one conversation — where we find your first win together.
A hands-on engagement that restructures how your business runs with a remote team, AI, and automation. We start with one high-value area, prove it on real results, then scale what works.