Frontier LLMs are genuinely good at parts of investment analysis, and pretending otherwise would be silly — Misano itself runs on Claude and OpenAI models. The honest question is not 'AI or no AI' but which jobs a raw chat window does well, and which jobs quietly fail in ways you only discover later.
What a raw LLM chat is genuinely good at
- A qualitative first pass: summarize this teaser, list the risks, what would a skeptic ask.
- Market context: how this sector works and what typically drives value in it.
- Drafting: turning your conclusions into a readable memo.
Where the chat window quietly breaks
- Scanned filings: Czech (and most European) registry statements are often photographed paper. Chat uploads process images at premium token prices, and the numbers come back with silent errors — a decimal comma read as thousands separator, a 'v tisících Kč' scale missed. Nothing warns you.
- No memory across companies: every valuation starts from zero. The same public filing is re-read — and re-paid for — every time anyone asks about it.
- Inconsistent scoring: ask the same model about two companies on two days and the implicit criteria drift. Ranking a pipeline requires one versioned model applied to everything.
- Math in the wrong place: an LLM doing a DCF in its head is a liability. The projection arithmetic, WACC build-up and terminal-value cap belong in deterministic code; the model's job is judgement, not multiplication.
- Invented multiples: ask for 'the sector multiple' and you get a plausible number with no provenance. A benchmark you can inspect — including its evidence quality — is the difference between a valuation and an opinion.
What a purpose-built layer adds
The economics come from routing and reuse, not magic. Misano routes each job to the cheapest model that does it well — text extraction is not a frontier-model problem, investment judgement is. Public filings are parsed once and cached for every user; each analysis job is metered instead of open-ended chat. And the deterministic parts — DCF math, ratio computation, hard gates, the terminal-growth cap — run as code the AI cannot hallucinate around. The result, costed line by line in the founder's blog post, is roughly 90% cheaper per company than doing the same work in your own chat subscription — with guards a chat window doesn't have.
The honest boundary: if you evaluate one or two companies a year, a chat window plus a methodology guide is fine, and cheaper. The platform pays for itself when there is a pipeline — when consistency across companies, registry ingestion, and not re-paying for the same documents start to dominate the cost of thinking.