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Misano · Guides

Methodology, data, and thinking.

How to value private companies, work with Czech registry data, and run a disciplined pipeline — plus the full valuation benchmark Misano works with, and essays by the founder. References carry an update date and stay current; essays carry their publish date.

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All guides
dataEV/EBITDAEV/Sales

Valuation multiples — the full benchmark, published

EV/EBITDA and EV/Sales bands for 82 NACE divisions and 62 curated niches — the numbers the Misano engine works with, in full, with evidence grades and machine-readable JSON.

Misano · Updated 3 July 2026
valuationmultiplesDCFmethodology

How we value companies at scale

“NACE comparables don’t really work” — and yet we build on them. The full methodology Misano runs across 100, 500 or 10,000 companies at once: six layers, each fixing the blind spot of the one before it.

Misano · 4 July 2026
valuationEV/EBITDADCFmethodology

How to value a private company: multiples and DCF, the Damodaran way

Two methods carry almost every SME valuation: a market multiple on normalized EBITDA, and a DCF as the intrinsic cross-check. Here is the full working method — including the discounts everyone forgets.

Misano · Updated 3 July 2026
obchodní rejstříkARESdue diligencedata

Czech company registry data: what is public and how to extract it

Czech companies are unusually transparent on paper: identity, ownership, and years of financial statements sit in public registries. In practice the data is scattered, scanned, and full of format traps. A field guide.

Misano · Updated 3 July 2026
dealflowscoringpipelineprocess

Running a deal pipeline: sourcing, screening, and scoring against your thesis

The difference between a dealflow and a pile of PDFs is a pipeline with explicit stages and one scoring model applied to everything. The method, sized for a team of one to ten.

Misano · Updated 3 July 2026
AIvaluationLLMtooling

Valuing companies with AI: when ChatGPT is enough — and when it isn't

You can paste financials into ChatGPT or Claude and get a passable first opinion. Whether that is enough depends on what breaks: scanned filings, unit-scale traps, inconsistent scoring, and re-paying for the same documents. An honest map.

Misano · Updated 3 July 2026
scoringtransparencymethodologybuild in public

How I compute the Score: five dimensions, the full breakdown.

The Score is one number, 0–100. Instead of leaving it a black box, I show exactly how it's built: five dimensions, what's computed by math and what the AI judges, and the whole thing worked through on a real company.

Marek Kříž · 17 June 2026
AI coststransparencyPDFbuild in public

How to pay 90% less to analyse one company than in your own Claude/ChatGPT.

I wanted to drive down one number: what it costs to analyse a single company. Here, on real numbers, are the four places Misano cuts the cost of AI — and what that means for you.

Marek Kříž · 17 June 2026