Proof
Proof of the work.
Three categories of evidence, each labelled with where it came from. Real numbers, no rounding up.
Most consulting sites lead with anonymous case studies you can't verify. We'd rather show you the actual track record — enterprise transformations from the day job, operator-owned businesses we've helped, and the methodology applied to ourselves.
Enterprise transformations · Director of Analytics, enterprise environment
Compressing time at scale.
Outcomes from an enterprise analytics role supporting more than a dozen business units — not TLG client engagements, but evidence the methodology holds at enterprise scale.
7 days
→
30 minutes
Reporting cycle automation.
A multi-day manual reporting cycle consumed a full week of operator time every period. The pipeline was the problem, not the report — so I automated the extraction, unified the reporting layer, and built a dashboard the operator could read at a glance. The cycle now runs in 30 minutes, and more accurately, because manual transcription error was designed out.
5 business days
→
24 minutes
Client intelligence pipeline rebuild.
A five-day intelligence process spanning multiple tools and teams was the slowest link in the chain. I built and deployed an automated platform producing same-day insight — replacing legacy ticketing, dashboarding, and email coordination with a single automated path. Five days of waiting became 24 minutes.
Beyond time
Systems that change the math.
Manufacturing time isn't only about hours. The same systems discipline — data-driven targeting, automated modeling, smarter resource allocation — has consistently cut spend while improving results in the same budget envelope. When you fix the underlying process, the economics move with it.
The methodology · applied to owner-operated businesses
What the methodology looks like for a real owner.
The same method, run at owner-operator scale — where the owner is still the bottleneck and every hour is visible. Industries and outcomes are real; client names are kept private. The founding-cohort case studies are underway now, and those will appear here named, with measured before-and-after numbers.
100+ hours
→
a vacation back
Automated messaging and scheduling for an operator-owner.
Operator-owner spending nights and weekends triaging inbound messages and coordinating his calendar. The job had become 24/7 and a real vacation hadn’t happened in years. Built an automated messaging and scheduling system that handled the inbound triage and calendar coordination together. Time recovered: more than 100 hours a year — the equivalent of two full work weeks. He took the vacation.
~1 hour/day
→
14% productivity
Finding the leak hiding inside an errand.
Crews were taking the company truck to a convenience store at lunch — justified as a gas run. The real cost was nearly two hours of jobsite time a day, hidden inside an errand that sounded necessary. They were spending gas to go get gas, and burning productive time on top of it.
The lever wasn't software — it was seeing the leak. We brought lunch to the site from the same store. Crews stayed, the team loved the perk, and we recovered about an hour of productive time per crew member per day — roughly 14% against a working day, even after accounting for the food cost. Profit went up.
Manufacturing time isn't always automation. Sometimes it's removing the reason to leave.
Self-applied · The personal foundry
The methodology, applied to ourselves first.
Josh applies SAVE to his own work continuously. The site doesn't claim he's perfect at this — it claims he practices what he sells.
3+ hours/week
→
recovered
Inbox prioritization — an experiment that was shut off.
Built an automated email triage system that routed incoming mail into priority lanes by sender, subject pattern and follow-up rule. Most mail never reached the main inbox, and it recovered 3+ hours a week of attention. It no longer runs: the environment it was built in stopped permitting AI access to the mailbox. Kept here because the result was real and the constraint is instructive — a system you don't control can be switched off, which is its own kind of time risk.
The pattern
Different businesses. Different scales. Same methodology.
If we can compress 7 days into 30 minutes inside an enterprise data pipeline, give an operator-owner two work weeks of vacation back, and redesign labor flow on an HVAC jobsite — we can almost certainly find hours in your operation.
Find yours.
The discovery call is where we figure out what your version looks like.