Uncertainty → Trust → Adoption

Nobody uses
a black box.

Tomasz Czarnecki Tomasz Czarnecki
Tomasz Czarnecki
AI Product Manager & Builder

I build AI products you can argue with. Ex-Heineken AI Hub.

Inside the box

It's all probabilities.

The model gives a range.
People want an answer.
I build what sits in between.

01Highlights

First person in, three times.

The first hire at a Chinese architecture firm's European office, set up in Poland from zero. One of two founding members of MAD Architects' parametric design group in Beijing. The first product person in Heineken's Global Analytics & AI Hub. The fourth time, it's mine: Yaga Labs.

02Playbook

Nobody acts on a black-box answer.

What worked for us at Heineken, and how I build agents now: the model proposes, the rules check it, and the people who use it can see why.

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Worked at and built for
Heineken MAD Architects AREP Macopedia Pearson Macmillan Education Raben
03Case studies

Heineken spends €7.9 million on marketing and sales a day.

Someone decides where every one of those euros goes.
Before the hub, that was spreadsheets and gut feel.

Today so far · since midnight, your time
€0
€2.9B a year ÷ 365 · FY2024 marketing & selling · Source ↗

We asked 3 questions, in 1 loop.

01 · Allocation AI
Where?
Wherever the response curve says the next euro pays back best, by OpCo, brand and channel.
02 · Promo Advisor
What?
Whichever offer, mechanic and price simulate the best lift, tested before they ever launch.
03 · Smart Flighting
When?
Whenever the AI response curve says that spend actually converts, week by week.
Measured lift, fed back 04 · next year's plan starts here
04Yaga Labs
Pani Yaga: an old witch with red curly hair, round black glasses, a YAGA LABS hoodie, an embroidered folk skirt and a birch broom

She complains about AI. Then she ships it.

Yaga Labs is my experimentation platform: agents, small AI tools and black boxes you can argue with. Pani Yaga is its conscience. She has opinions about your model, and she's usually right.

05Deep dive

Back in China,
the fitness function was the eval.

Five 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.

The China years
MAD Architects · Xiamen, 2015–2016

Xinhee Design Center

Six office arms around a central atrium, wrapped in a floating PTFE skin. My part: the parametric model that generated the double-curved membrane facade and made it buildable, optimising membrane cost and buildability at once.

See the built building ↗
The built Xinhee Design Center at dusk: translucent PTFE membrane shells over the office arms, Xiamen.

Optimal was easy. Buildable was the job.

MAD Architects · Harbin2015

Harbin Opera House

My part: parametric timber interior, detailed down to fabrication.

Harbin Opera House from above, curving along the Songhua River wetlands
AREP · Shenzhen2016–2017

Gangxia North hub

My part: parametric skylights and entrances for the competition win.

Gangxia North hub: the 'Eye of Shenzhen' lattice skylight above the main concourse
If this resonates, let's talk

Got a black box nobody uses?

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

LinkedIn ↗ tomasz@czarnecki.ai
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