My bias came from finance, controls, and systems.
Before rogacki.ai, my work sat inside finance, audit, controlling, and
business analysis. The useful questions were never abstract: close the
month, reconcile the system, explain the variance, defend the investment
case, and make the model usable by people who make decisions.
That background matters for AI work. A model claim is only useful if it
survives messy source data, review paths, controls, budgets, and the moment
someone senior asks what happens when the system is wrong.
I build because strategy without production contact gets soft.
I run rogacki.ai as an independent advisory practice for investors and
leadership teams working through AI diligence, readiness, and value-creation
questions.
My build context is hands-on: document intelligence systems, OCR and
extraction pipelines, review workflows, voice AI infrastructure, and EU AI
Act readiness work for regulated environments.
The useful questions are practical.
Which workflow is worth automating? What data does it touch? Where does
human review sit? What changes if the system is high-risk under EU rules?
Which vendor claim survives inspection?
Those questions rarely get answered by a generic AI strategy deck. They need
a builder's sense of where systems fail and a finance lens for what a
decision has to support.
Why the advisory and build brands stay separate.
I also run AROG AI:
an implementation studio for production AI systems. rogacki.ai is the
advisory layer: strategy, diligence, readiness, and builder judgment.