Engineer, founder and CAIO (Chief AI Officer).
Marcin is an engineer and entrepreneur working across machinery valuation, commercial B2B operations, and AI workflow automation. The "Engineer" in his title refers to his engineering and valuation background — he is not a conventional legacy software developer. His story has five threads:
Physical industrial assets demand zero tolerance for generative hallucination. Principles cultivated across more than 15 years of machinery and industrial asset valuation inform how AI boundary architecture is designed today.
Evaluating industrial equipment, production lines, chemical plants, corporate shares, and complex technical assets across European jurisdictions. Physical assets reveal the limits of polished documentation: real machinery arrives with missing serial plates, fragmented multi-lingual operating logs, uncurated specs, and disparate telemetry.
Commercial negotiation and asset liquidation do not permit speculative confidence scores. This background directly informs why production AI workflows should not operate as open-ended autonomous agents: they need boundary constraints, primary source grounding, and operational safeguards before executing real enterprise side-effects.
Capability description — structured boundaries.
Rule-constrained lead enrichment, qualification parameters, and boundary verification prior to CRM persistence. Human review triggers support sign-off on non-standard signals.
Structuring uncurated equipment dossiers, multi-language technical catalogues, and inbound machinery enquiries. The pipeline isolates extraction from actuation: no autonomous actuation of physical assets (design intent).
Topic research synthesis, primary citation grounding, and structured technical drafting. All synthetic material is isolated behind editorial review points prior to publication.
Diagnosing operational bottlenecks, framing verification envelopes, and orchestrating controlled pilots, including technical perimeter monitoring with chors.net (OWNED project).
Five sequential execution phases applied to every automation architecture. No speculative leapfrogging.
Map friction, human touchpoints, tacit knowledge, and dirty edge-case documentation before touching model layers.
STAGE 1 // MAPPINGIsolate repetitive cognitive toil from strategic judgment. Reject automation if complexity outweighs operator utility.
STAGE 2 // DELIMITATIONDeploy in read-only sandbox or sidecar testing environments. Stress-test hallucinations on adversarial inputs.
STAGE 3 // SANDBOXCheckpoint nodes with human-in-the-loop sign-off. High-stakes outputs are not committed autonomously without an operator — that is design intent; a review point holds only where it is technically enforced.
HUMAN REVIEW POINTProvide thorough runbooks, telemetry dashboards, and system ownership transition to internal team leads.
STAGE 5 // HANDOFFClarity of responsibility is essential. HAKER.SI operates as an open technical laboratory and publication venue, distinct from commercial advisory entities.
the open engineering practice publishing operational frameworks, architectural boundary specs, documented case structure, and honest failure modes.
the commercial AI advisory vehicle structuring, piloting, and maintaining enterprise workflows for client mandates.
RELATED INITIATIVES & PRACTICES — all OWNED projects (not endorsements):
Enterprise AI advisory practice. OWNED project.
Structured topic discovery, research aggregation, and citation-bound drafting tooling for publishing desks. OWNED project.
Collaborative technical perimeter monitoring and threat boundary analysis. OWNED project.
Machinery valuation, appraisal and secondary-market operations: technical cataloguing and commercial brokerage for used equipment. OWNED projects.
HAKER.SI rejects standard marketing copy, unverified vendor metrics, and hype cycles. All published technical architectures are strictly separated from hypothetical diagrams.
"We document system failure modes, rate limits, and verification costs with the same engineering rigor as throughput gains. If an AI node hallucinates under load, that failure is catalogued as a critical design metric."
Explore whether your technical documentation, B2B pipeline, or machinery data operations are appropriate candidates for reliable, human-gated AI systems.
Discuss the workflow, constraints and control points directly with Marcin Białczyk.
Discuss a workflow
