Tinystores — for Tobias

Zürich · 3 June 2026

Dear Tobias,

Since our lunch on the 19th of May — 15 days ago — I've been building you something. It is a small tool that scores any Swiss commercial-lease address for how well it would work as a Tiny Fish store — using the same signals your intuition already uses, but at the scale a brain cannot.

This page is the first version. It is private — just for you. The point is not the page; the point is to show you a direction, and ask whether you want me to push it further.

Jonathan

Prefer to listen? Here is the same content, in about eight minutes, broken into eleven short parts. The voice isn't really mine — but every word is.

1.Opening
2.A letter
3.Why I'm doing this
4.A question I keep returning to
5.What's on this page
6.The most interesting finding
7.Cities the van cannot reach
8.Scale to the world
9.What I don't yet know
10.Who I am, and what this is
11.Closing

What this is, in fourteen questions

Fourteen questions, in the order I think you'll have them.

Why am I doing this?

Because of a belief I hold quietly and seriously: everyone deserves a great sushi experience — fresh, elegant, respectful of Japanese culture and of the producers behind it — at a price that doesn't require an occasion. A healthy, beautiful meal at around twenty francs, drink included. Not eighty.

I applaud what you've built. Tiny Fish could become that in the Western world. A business with soul, meaning, purpose. A business that quietly changes how people think about food. It would show the world that even a tiny fish can have a big impact. Switzerland is a small country. But Switzerland has big ideas and big values. Switzerland has changed the world before. Henri Dunant founded the Red Cross. Henri Nestlé and Daniel Peter put Swiss food on the world's table. Auguste Piccard reached the top of the sky and the bottom of the ocean. The World Wide Web was born at CERN in Geneva.

What question do I keep returning to?

You have built one of Switzerland's most thoughtful fast-casual brands. Fourteen stores opened with brand discipline intact, kitchen logistics tight, a category genuinely better than what existed before — and you've been clear that the thoughtful pace is the advantage, not a constraint.

The question I keep returning to is what kind of tool, alongside the judgement you already apply, could help make the next sixteen lease decisions land as well as the first fourteen. I don't yet know how Tiny Fish currently selects sites — only that you do it well. This page is my attempt at one such tool. It is not a verdict on the way you work.

What if there were one more lens?

Imagine being able to score every commercial-lease listing that appeared on the market this week — ranked, with the reasons attached — before any visit. A weekly digest each Monday morning, sitting next to the broker relationships and instinct you already trust, not replacing them. A second pair of eyes on the long-list, so the short-list arrives already pre-filtered against your bar.

Imagine the same model, two years from now, doing the same thing in France, Germany, the United Kingdom — pre-positioning the map before franchise partners are even ready. The thoughtful pace, preserved at international scale, with the human judgement still at the centre.

What is my approach?

Build an opinionated scoring tool — one that thinks the way you think when you stand in front of a candidate site, but at the scale of every available commercial-lease address across Switzerland.

The model holds your filters: footprint, lease term, distance from existing stores, kitchen reachability. Inside those filters, it ranks candidates by the same intuitions you carry — pedestrian density, catchment wealth, rent versus revenue feasibility, transit access, direct competition.

This is one possible approach. If your existing process already covers most of this, that is likely the better answer.

What are my values?

Build for impact, not for hours billed. Anything I work on has to compound — a system, not a slide deck.

Be honest about what the tool knows and what it doesn't. A scoring model is most credible when it can disagree with the chooser. This one already does.

Never sell what isn't ready. I'd rather under-promise and ship something solid than over-pitch and disappoint.

What does this page contain?

Six things, all visible below these questions.

A scoring engine that runs end-to-end on any Swiss address. A live map of your fourteen existing stores, plus the SV Group fridges and the Altstetten kitchen. The same engine applied to those fourteen stores as an honesty check. A ranked list of new candidate locations the engine is currently converging on. A specific recommendation on what to do next — including whether to keep delivering by van or commit to delivering by train. And, below all of that, my actual proposal.

How does it work, in plain terms?

The model takes many independent public signals about each address and turns them into one score. Each signal is normalised to a 0-100 scale, weighted by how strongly it predicts a sushi-store's commercial success, and combined. The output is one composite score per address, with the full breakdown visible. Signals only get trusted when several of them agree.

What kind of data does it look at?

Only public, freely available data — no broker fees, no expensive panels. The categories are:

How many pedestrians actually walk past the address. How wealthy the neighborhood is, including the specific household-income figure that captures wealthy Swiss districts cleanly. What the building and the street look like, read automatically from a recent photo of the place. What the restaurants around the address charge for a meal. What commercial rent costs in that postcode. How fast you can reach the address from Altstetten. How busy the nearest train and tram stops are. Whether the address can be reached by train if you ever wanted to deliver that way.

How does it know how wealthy a neighborhood is?

Six different signals are combined: how many luxury shops are nearby, how many private banks are nearby, whether any premium employer — UBS, McKinsey, Pictet, Goldman Sachs — has its headquarters within walking distance, what the surrounding restaurants charge for lunch, the official household income for the district, and the automatic read of a recent street photograph.

The sweet spot is not pure luxury. A street with only Patek shops and no offices is wrong too. The sweet spot is wealthy people with a lunch break — professional districts with serious money and serious meal-break schedules. Bleicherweg. Bern Spitalgasse. Stadelhofen.

Does the tool agree with your existing stores?

Mostly yes — and the disagreements are useful.

Running the same unbiased tool on all fourteen existing stores ranks Löwenstrasse, Bern Spitalgasse, Bleicherweg and Stadelhofen at the top — exactly where your customer review counts put them. The tool's ranking agrees with your customers' ranking strongly.

One existing store comes out at the bottom. The tool says it was a stretch. I take that as the honest version of validation. A tool that only ever agrees with the chooser is useless; this one earns the right to be trusted by also surfacing the picks that are weaker.

What is the most interesting finding so far?

Bleicherweg — your flagship by review count — only ranked in the middle of the pack until two signals were added: the household income at the Zurich-district level, and an automated analysis of an actual recent photograph of Bleicherweg from the street.

The photograph analysis came back as professional lunch crowd, modern premium building, UBS signage detected, affluence 85/100. Once those two signals were in, Bleicherweg climbed to second of the existing fourteen — right behind Löwenstrasse, which sits on top of Switzerland's busiest station. That is exactly the kind of correction the model is designed to make.

What about cities the van can't reach?

This is the real strategic question, and the answer changed my mind during the build.

With one Altstetten kitchen and a three-hour driving radius, the model says full national coverage caps around eighteen to twenty stores. The Romandie and Tessin are mostly outside the window. If Tiny Fish commits to rail-based delivery — passenger InterCity trains plus insulated cold-chain boxes plus a local Velokurier handover at the destination station — then Geneva, Lausanne, Basel, Bern and Lugano all come back inside reach, and the model jumps from eighteen-to-twenty stores to thirty-plus from a single kitchen.

Three options, framed cleanly:

Status quo (vans only). Keep the road model, accept the cap. Realistic 2030 footprint around eighteen to twenty stores. Romandie and Tessin stay mostly outside the window.
A second kitchen. Build a Romandie production hub. Around one million in capex plus a second food-safety perimeter to manage. Full national coverage on the road model, but every Western Swiss store has to carry its share of that capex.
A rail pilot. Thirty to ninety days, one Geneva store, around thirty thousand all-in including the insulated container and the Velokurier contract. Decide on measured cold-chain performance and unit cost, not on theory.

The honest recommendation, if you wanted my read, is the pilot. Modest cost. Asymmetric upside. It would answer a question that's otherwise hard to settle in theory.

Can this scale to the world?

Yes — and that is the part I'm most confident about.

At lunch you said something that stayed with me: that three kitchens could cover all of Germany. That's exactly how this model would approach Germany too — scoring Munich, Berlin, Hamburg, Frankfurt, Düsseldorf against each other before any kitchen is sited, then pre-positioning the three footprints to minimise van-radius gaps. The decision becomes a map you can read, not a spreadsheet you have to defend.

The Swiss build is the proof of method. The same engine extends to France, the United Kingdom, Italy as franchise partners come online — Paris, London, Milan. Luxury brands are luxury everywhere. Restaurant pricing works in any currency. Public mapping and government statistics exist in every European country. Recent street photography is available globally.

Building this in a new country is known work — many datasets to discover, normalise, and weave together with the same data-science craft. None of that is free — the model is hard, careful work. What it offers in return is a layer of scalability that no human reviewer can carry alone. Alongside judgement, not instead of it.

What don't I yet know?

I don't yet know how Tiny Fish currently chooses its sites. You may already have built something careful — internal scoring sheets, broker networks tuned over years, instinct calibrated by hundreds of visits. Much of what's on this page could be redundant to that, and I'd rather find out than assume.

If we talk, I'd be glad to learn what you already use — and to see honestly where the model on this page fits alongside it, where it doesn't, and where it might earn a place.

Where Tiny Fish is today

The fourteen stores, numbered, plus the SV Group fridges and the Altstetten kitchen. Click any pin to open it on Google Maps.

Numbered store (1–14) SV Group Fridge Altstetten kitchen

Your fourteen stores, scored by the same tool

Same model, applied to the stores you already chose. No special weighting, no preferential treatment. The tool's ranking lines up strongly with how often your customers leave reviews.

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Top candidates the tool is converging on

Score is the model's composite 0-100. A yellow score (50-65) is mid-tier. Red (under 50) means a hard filter penalty kicked in — usually either the kitchen is too far by van, or the candidate sits inside another existing store's catchment.

The ten progress bars at the bottom of each card break the composite score into its dimensions. Every bar reads the same way — further right means better for a Tiny Fish store. Rent affordability and competition are scored that way too (high = cheap rent, high = low competition).

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And the candidates the tool rejected

Twelve candidates that hit a hard filter — kitchen van too far, or sitting inside another store's catchment, or rent versus revenue not feasible. Shown so the model's "no" is as visible as its "yes".

Who I am, and what this is

I'm a humanist first. What drives me is reaching big goals with the smallest resources possible. The way to do that is to build careful design into the system itself. Simplicity is the ultimate sophistication. And it is the most likely path to real positive impact at world scale. This is what scalability means. One well-built kitchen becomes a hundred. One good meal becomes a billion. A billion lives that ate better, over a generation, because the model held. Money matters to me. Not for hoarding. Not for a beach retirement. Money is the ammunition to multiply impact. Capital, success, money — these are the multiplier, not the prize. Tiny Fish, at the scale you're aiming for, is exactly that kind of thing.

The way I'm equipped to do that: as a financial analyst — a Chartered Financial Analyst charterholder — an investment and portfolio manager by training, and a data scientist and software developer by craft.

What I'd love next, if you're open to it: a short conversation, on a walk or over coffee, in the next couple of weeks. I'd love to hear what you think of what I built.

I'd also love to hear how you currently approach store selection — what tools you already use, what you've tried, what's worked. The model on this page is a starting point for that conversation, not a proposal that replaces anything.

There is no offer here yet, and no ask. My main focus right now is a personal venture I'm building in parallel. This dashboard is a one-time intellectual exploration of your category — not a pilot, not a pattern. If anything formal between us ever makes sense, it will come from a different chair, at a later time, priced on its own terms.

And you — why are you doing this?

Jonathan

I'll keep this page live until 1 August 2026, then retire it unless we've found a reason to keep it.