Dealer Flywheel
Operational intelligence and technology implementation for automotive dealerships. Built to find where performance breaks between systems, people, information, timing and ownership, then help implement and measure the fix.
Built
Louis Scalise
I build intelligent systems that do real work.
AI implementation and systems engineering
I find the constraint in an operation, then build a system designed to prove what works.
I don't start with AI.
I start with the problem, the system around it, the assumptions we're making, and the outcome we're trying to produce. Then I build what we need to test whether we're right.
01 / 06 · Problem
What outcome are we trying to produce, and how does it happen today?
02 / 06 · System
Map the people, process, technology, data, timing and ownership around it.
03 / 06 · Hypothesis
Changing X should produce Y, because of Z.
04 / 06 · Build
Something real enough to test the riskiest assumption.
05 / 06 · Evidence
Compare what actually changed with what we expected.
06 / 06 · Learn
Understand why, find the next constraint, and go again.
A workflow shows the sequence.
This happens, then this, then this. It's usually where we start when we try to understand how work gets done.
Fig. 02
A system shows what the sequence depends on.
The people doing the work. The information they need. The technology connecting it. The timing, ownership and decisions between each step.
Fig. 03
The failures usually live between them.
A record falls between systems. Information arrives too late. Ownership changes without anyone noticing. Every part can be working exactly as designed while the system still fails.
Work.
AI Recruiting System
An assistant runs first contact and screening. Rules hold the hard requirements, every recommendation keeps a decision trace, and a recruiter decides anything uncertain.
PrototypeSynthetic data
Find the Constraint
Automate any step of a pipeline and watch what happens to the output.
Experiment
I'm Louis Scalise.
I spent more than 15 years in sales, marketing, finance and business operations before studying AI engineering. Today I combine that experience with hands-on systems development.
I'm interested in difficult problems at the intersection of business, technology and AI.
More about me →Have a difficult problem worth understanding?
If you're trying to figure out where AI actually belongs in your operation—or you know what you want to build but need someone to think through the system with you—I'd like to hear about it.
me@louisscalise.com