DNA

Numbers don’t lie.

That’s why every business — regardless of industry, scale, or complexity — can be turned into a precise model. You just need enough patience to build it and enough experience to ask the right questions. And the most interesting solutions are always the ones that didn’t have a name yet when they were created.

200+

dedicated financial models with full P&L, balance sheet and Cash Flow structure

€500M+

revenue from a platform built from scratch on a custom system architecture

Enterprise

implementations for market leaders, including Orange S.A.

Venture Capital

Agility that stays forever.

Working on the evaluation of dozens of companies in a short time gives you something no course or certificate can provide: the ability to step into an unfamiliar business and see in one hour what the owner sees after a year. Where the value is, where the risk is, which assumptions are real and which are wishful thinking. This kind of company reading stays with you — and today shapes every task and every goal.

Private Equity & Angel

Precision that does not accept approximations.

Due diligence in a PE environment is not analysis — it’s a reconstruction of the company from the inside. Every metric must be justifiable. Every forecast must stem from assumptions, not optimism. The result is a work standard that doesn’t compromise, regardless of project scale — whether it’s valuing an enterprise or calculating the cost of producing one litre of milk on a specific farm.
Similar experience with Angel funds — smaller transaction scale, the same intolerance for approximations. An individual investor asks exactly the same questions as a PE fund — they just often have less patience for the answers.

You don’t need to explain how the company works.
It’s enough to say what doesn’t work.

Interim CFO

When a company needs a CFO, but not a full-time position.

Investment experience also translates into direct financial support for companies — from liquidity management, budgeting and cash flow, through controlling and management reporting, profitability analysis and investment evaluation, to cooperation with banks, auditors and advisors. Full CFO scope — without building an entire financial structure.
Every project is approached with full identification — what is created should be something to be proud of, not something to hand over and forget.

Evolution

From spreadsheet to architecture.

It all started with Excel — and Excel is still at the center. Not because other tools are lacking, but because a model you understand completely is worth more than a system you don’t control. Three million cells, all connected, all reproducing the real operational process — this is not a spreadsheet. It’s a way of thinking recorded in formulas.
Over time, Excel stopped being enough — not because it’s weak, but because the problems grew. The result of this collision is the architecture that today defines the way of working at Origami Effect.

Foundation

Excel as a way of thinking

Complex financial-operational models in pure Excel. A spreadsheet that understands the business better than many ERPs.

Extension

Excel + MongoDB + API

Data lives in one place. Excel reads it, the dashboard displays it, the algorithm processes it. Not replacement — extension.

Now

Single Entry – Multi Output

One source of truth. The financial model, operational dashboard, ML forecast and investor report all read exactly the same data. Zero discrepancies. One truth, many uses.

Direction

Where this is heading.

The line between operational and financial data is disappearing. Systems that used to live separately — controlling, production, sales, forecasting — are beginning to speak one language. The result is something most companies have never seen: numbers that explain themselves.

Operational-financial data as input for AI

Precise calculations feed into language models that turn them into narrative form. Management receives not a table, but a ready-made analysis in the language of decisions — for those who need answers, not numbers.

Data orchestration in Python

Not scripts — a data flow architecture. Automation that connects sources, transforms, verifies and delivers data where it’s needed, when it’s needed.

Inventiveness and execution

Here we don’t look for a tool in a catalog. Here we design a solution from scratch if none of the existing ones fit. And we deliver it to the end.

An idea without execution is just talk.
Execution without an idea is just cost.

Portfolio Example – AgriTech

When the model needs to know more than a human can remember.

The financial-operational model for a farm begins in the field, at the moment of sowing corn. The real architectural challenge, however, appears after the harvest, with the seemingly trivial decision: what to do with the collected straw? Sell it? Plough it under as fertilizer? Leave it as bedding for the animals?
To make this decision optimally, the model cannot look only at the current quarter — it must precisely simulate reality over a 60-month horizon. The system pulls data on herd size, age structure of animals and breeding schedule for the coming years, because these determine future demand for welfare and bedding.
The scenario becomes even more complex when, within the same 5-year planning window, there is a need to replace the machinery fleet. Introducing a new forage harvester dramatically changes harvesting efficiency and time, which directly affects the quality and energy value of the produced feed. The model must immediately recalculate this operational gain and confront it with financial mathematics.
What is more beneficial in the farm’s tax structure: operating or financial leasing? The system simulates both variants — breaks down instalment costs, takes into account depreciation write-offs, tax shields and own contributions, then passes this cost through the entire P&L and Cash Flow structure.
A wrong decision at one point triggers a domino effect: it changes the demand for external feed components, modifies the nitrogen balance for direct subsidies and ultimately impacts the technical cost of producing one litre of milk — calculated to four decimal places. Three million formula cells were not created out of a love for complexity. They simply recreate the farmer’s real, interconnected decision ecosystem for the next 5 years.
The same approach is used when checking which bricks affect the profitability of hotel rooms, whether a dormitory in a given location makes financial sense, or how a decision in one department will translate into margin in the next quarter. The industry changes. The method remains.

Philosophy

What we believe in here.

Essentialism.

Eliminate noise, preserve signal. Simplicity is the result of deep understanding — not its substitute.

Innovation through connection.

Finance with code. Operations with ML. Field data with an investment model. Creativity is seeing that connection before anyone else does.

Patience as a quality standard.

Three million cells is not an exaggeration — it’s exactly how many are needed for the model to be true. Shortcuts are immediately visible in the quality of conclusions.

The tool is chosen for the problem. Not the other way around.

Excel, Python, React, ML, GPT — each when it makes sense. Never because it’s fashionable.

Simple tools for complex structures.

If you are building a company that should know more about itself than it does today — this is the right place.
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