ABOUT JOVAN

Who am I?

I'm Jovan Filipović — an Electrical Engineering graduate from the University of Belgrade, currently continuing my master’s studies while building practical AI agent systems for small businesses. My work focuses on messy lead sources, CRM/ERP data, repetitive sales follow-up, and operational bottlenecks that still depend on manual work or disconnected tools.

Alongside engineering and AI, years of basketball and team captaincy shaped how I think about leadership, discipline, teamwork, and execution under pressure. NLP and communication training also influence how I design clearer outreach, better customer conversations, and human-in-the-loop workflows.

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MY PATH

Systems thinking, applied to messy business workflows.

My background started in Electrical Engineering, where I learned to think in systems: signals, control, automation, robotics, data, constraints, and structured problem solving.

Over time, I became more interested in how AI can be used outside demos — inside real business workflows where teams deal with messy data, repetitive follow-up, unclear priorities, and too much manual work.

That is why my projects focus on practical AI agent systems: lead scoring, ERP/customer data cleanup, follow-up preparation, planning loops, evaluator loops, and human approval workflows.

NLP, PCM, communication training, and basketball leadership also shaped the way I think about adoption. A useful AI system is not only technically correct — it has to be understandable, reviewable, and easy for people to trust.

MOMENTS

Background that supports practical AI work.

A compact look at the experiences behind the way I build and communicate.

Engineering background

A technical foundation in systems, control, automation, robotics, data, and structured problem solving.

Team leadership

Experience organizing people, keeping standards clear, and making decisions under pressure.

Building practical AI systems

Hands-on work with agent workflows, evaluators, structured outputs, local demos, and portfolio-ready MVPs.

Communication & business development

Practice turning technical ideas into clear conversations, outreach, and practical business framing.

PRINCIPLES

What shapes my work

The standards I keep returning to when building AI workflows.

Systems thinking

Before adding AI, I try to understand the workflow as a system: inputs, constraints, handoffs, feedback loops, and the points where decisions actually happen.

Human-approved automation

I prefer AI systems that prepare, score, summarize, and evaluate work — while keeping important decisions reviewable and approved by a human.

Business-first AI

The goal is not to add AI for its own sake. The goal is to save time, make priorities clearer, clean up data, improve follow-up, and reduce operational noise.

Execution under pressure

Basketball and team captaincy taught me to keep standards clear, communicate simply, and move forward with practical next steps when conditions are not perfect.

OUTSIDE WORK

Being team captain taught me how to execute under pressure.

As captain of the ETF basketball team, I learned how to organize people, keep standards clear, communicate under pressure, coordinate responsibilities, and move a team toward a shared goal. That same mindset shows up in how I build AI systems: clear structure, practical execution, and human decision-making at the center.

CURRENT FOCUS

Where I am focused now

AI automation for small businessesAgentic workflowsCRM/ERP data workflowsLead prioritizationOutreach systemsMSc Automation and Control Engineering — Politecnico di Milano

SELECTED WORK

See the systems behind the thinking.

Explore the AI workflow projects that turn this approach into practical systems.

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