Proof of work

Evidence, not theater.

This page is intentionally not a case-study carousel. It is a compact record of currently approved proof statements and Adam-owned work.

Adam-owned illustrative

OCR Portal for CRE document intelligence

An Adam-owned document intelligence product for Polish commercial real estate workflows.

The core proof is not a slide. It is a working document intelligence system: document intake, OCR, extraction, review, and structured outputs for real estate diligence and lease-administration workflows.

What it proves:

  • Adam understands messy document workflows, not just strategy language.
  • The advisory point of view is grounded in model behavior, review UX, data quality, and production constraints.
  • AI Act and GDPR conversations are tied to actual system boundaries, not abstract compliance theory.

Public proof is intentionally limited to Adam-owned system evidence and safe artifact surfaces. Client-specific references stay private unless explicitly approved.

Redacted in preparation

EU AI Act readiness and diligence

A practical advisory lens for classifying AI systems, mapping controls, and turning regulation into board-level decisions.

The advisory work starts with system boundaries: what the AI system does, what data it touches, who reviews the output, and what failure would mean for the company.

What it proves:

  • AI Act readiness is treated as an implementation and governance question, not a generic compliance slide.
  • Diligence focuses on system inventory, risk class, vendor exposure, review paths, and controls a board can actually understand.
  • Public detail stays bounded until redacted memos or approved artifacts can be shown without implying client proof that has not been approved.

The current public proof is the advisory method and the implementation judgment behind it. A redacted artifact can be added only after the evidence is safe to show.

Adam-owned illustrative

Production AI systems built

Hands-on context from document intelligence, extraction workflows, voice AI infrastructure, and review systems that had to behave like software.

The build context behind rogacki.ai matters because most AI strategy fails at the same boundary: source data is messy, review states are unclear, vendors overclaim, and prototypes do not behave like production systems.

What it proves:

  • The advisory point of view comes from shipping workflows, not only evaluating them from the outside.
  • Risk is read through concrete surfaces: data quality, human review, latency, extraction accuracy, vendor boundaries, and rollout constraints.
  • Implementation and advisory stay in separate brands, so diligence advice can stay sober even when a build path is possible.

The public proof shows categories of work Adam can stand behind without borrowing credibility from unapproved names.

Proof should make the first call sharper.

Send the company, workflow, or investment question. The call should leave you with a clearer next decision.

AI assistant, not Adam's voice.