Tomasz Czarnecki
Tomasz Czarnecki Founding AI Product Lead · now building at Yaga Labs

I open black boxes.

I build AI people can check. Forecasts, agents and decision tools that people actually trust and use.

  • 1stProduct person in Heineken’s Global Analytics & AI Hub
  • 3AI systems shipped at Heineken
  • 36%ROI improvement across brands and touchpoints, per Heineken
  • 17+Markets shipped with Promo Advisor
  • 8 yearsWorking in China, client-facing in Mandarin
01HeinekenHeineken · Global Analytics & AI Hub

Decision tools people actually use

Heineken spends about €7.9 million on marketing and sales a day. Someone decides where every one of those euros goes. The models were ready early. The people deciding still trusted their spreadsheets more.

Spent since midnight, your time €0 ~€2.9B a year ÷ 365 · FY2024 marketing & selling · Source

Case studies

Where to spend the marketing budget.

  • Bayesian allocation across markets, brands and channels.
  • More profitable spend with incremental gross profit reported in Mexico, Brazil and the Netherlands.
  • 36%ROI improvement across brands and touchpoints, per Heineken
  • 3Markets with gross-profit lift
  • BayesianResponse-curve model
Read the Allocation AI case study →
Allocation AI response-curve view: current spend sits past the point of peak ROI.

Then the measured lift decides next year’s budget.

02AboutNinety seconds, in person

First person in, three times.

The first hire at a Chinese architecture firm’s Polish office, set up in Poland from zero. One of two co-founders of MAD Architects’ computational design group in Beijing. The first product person in Heineken’s Global Analytics & AI Hub. Each time, the hard part wasn’t building the thing. It was getting people to use it.

Ninety seconds, in person.

Worked at and built for

Heineken MAD Architects AREP Macopedia Pearson Macmillan Education Raben
03SkillsShape · Build · Bridge

Three edges, one centre.

I trained as an architect and wrote the parametric tools behind double-curved facades in Beijing. I spent eight years in China. Based in Guizhou as the client-facing lead, I brought in a ~70M RMB residential project in Mandarin, with a design team in Poland behind me. Now I build with AI agents, hands-on, every day. Product is where the three meet: I can shape what people see, earn the trust of the people who will use it, and build the first version myself.

  • ShapeArchitecture, 3D, parametric
  • BuildAI, agents, open source
  • BridgeMandarin, China ↔ Europe
  • Product3 AI systems shipped at Heineken
04BuildingBuilding with AI

One person, a few agents.

I build with Claude Code and AI agents. Spec first: I write the spec, coding agents build, and I review every change. One rule across everything: the model reads and summarises, deterministic code decides.

Platform

Yaga.sh

My experimentation platform. Where I experiment and build things.

  • Agents, small AI tools and black boxes you can argue with.
  • Miss Yaga is its conscience. She has opinions about your model.
Visit Yaga.sh ↗
Yaga.sh homepage: ‘She complains about AI. Then she ships it.’ beside Miss Yaga with her birch broom

Miss Yaga says

“Three hundred years I read tea leaves. Now they call it a confidence interval.”

1 / 8
05ChinaAlgorithmic Design

Back in China, the fitness function was the eval.

Eight years working in China, back and forth ever since. I wrote the scripts behind a facade and an opera house interior at MAD, and competition wins at AREP. A decade before “AI agent” was a phrase, I was building evolutionary solvers that bred building geometry from a fitness function.

Selected buildings

Images courtesy of MAD Architects and AREP

Live model The Xinhee facade, evolving An evolutionary solver breeds the membrane geometry, scoring every generation on cost and buildability.
  • MAD Architects team photo in front of the Beijing office
    MAD family
  • Tomasz laughing with teammates around a monitor at MAD
    BaiZiWan team
  • Tomasz with five MAD colleagues after work
    After work
  • Tomasz with his Gangxia North hub team in the office
    Gangxia team
  • Tomasz lying on the model table, adjusting a massing model
    On the model

Optimal was easy. Buildable was the job.

06Contact

Got a black box nobody uses?

Tell me what it predicts and who ignores it. I’ll tell you what I’d build first.

tomasz@czarnecki.ai