Stock valuation that shows its work

We run the right valuation model for each company — not one formula applied to everything — and explain every assumption so you can verify it yourself.

21 valuation methods. Sector-aware routing. Built by finance + ML practitioners.

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21 valuation methods Sector-aware routing Full provenance AI exploration

Three things wrong with stock valuation today

📊

Screeners give you
a grade, not an answer

"Value: A+" — but which model? What assumptions? The same formula runs on a bank and a pharma company.

🤖

AI chatbots give you
plausible fiction

Ask ChatGPT "what is LLY worth?" and you get a confident answer with no model, no inputs, no sources.

📑

Analyst reports give you
a number to trust

Morningstar says $X. Great — but why? What if the terminal growth assumption is wrong? You can't interrogate a PDF.

The right model for each company

Real valuations from our platform — not mockups

LLY Primary
Pharma SOTP

Product-level revenue with patent-cliff curves. GLP-1 drugs modeled as going concerns because the obesity market has no LOE precedent.

Beta: Damodaran Pharma Pipeline: rNPV weighted
See full analysis →
JPM Primary
Justified Price-to-Book

Standard DCF doesn't work for banks — debt is the product, not a financing choice. Book equity is the anchor, cross-checked with residual income.

ROE: forward normalized CET1: regulatory check
See full analysis →
ISRG Primary
Installed-Base DCF + PVGO

Razor/blade business: 12,000 robotic systems creating recurring revenue. Monte Carlo growth options for cardiac expansion and Ion platform.

Base: procedure volume PVGO: 4 options modeled
See full analysis →

Rigorous analysis. Explained at your level.

Newcomer

"What even is a stock and why should I care?"

Beginner

"Walk me through how valuation works"

Intermediate

"Why does WACC-g sensitivity matter here?"

Practitioner

"Show me where this analysis is most fragile"

No stupid questions. Ask until you understand — not until the tool runs out of patience. Analyst reports assume you already know the jargon. Generic chatbots can't go deep. We do both: start simple, go as deep as you want, with the real data underneath.

Then ask it anything

Full demo — watch at your own pace

Why does pharma-SOTP give $1,372 but DCF gives $814 for Eli Lilly?
The $560 gap is pipeline rNPV. The DCF projects from aggregate revenue — it captures the existing GLP-1 trajectory but not the $31B from orforglipron (NDA filed) or $9.8B from retatrutide (Phase 3). The SOTP prices each product individually...
What if Mounjaro growth slows to 15%?
From the sensitivity table: fair value drops from $1,372 to ~$1,250 — a 9% reduction. The thesis still holds because Zepbound and pipeline provide 45% of total value...

Real analysis. Real numbers. Not generic AI output.

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Frequently asked questions

How is this different from asking ChatGPT to value a stock?
A general-purpose chatbot can give you a rough DCF, but it uses the same approach for every company, approximates key inputs, and can't show you where any number came from. Push deeper and the answers get vague. We run sector-specific models with sourced assumptions and pre-computed sensitivity analysis — and the AI explores from that real data, not from general knowledge.
Where does the data come from?
We use high-quality reference data sources and company-specific inputs from SEC filings and earnings reports. Every input is traceable in the analysis.
Is this investment advice?
No. ExplainValue is an educational and analytical tool. Our valuations are model outputs based on assumptions — not recommendations to buy or sell. We show the work so you can evaluate the assumptions yourself and form your own view. Always consult a qualified financial professional.
How often is the data updated?
Valuations are refreshed regularly through our cache warming pipeline. Each analysis shows the date it was generated and the price at that time. Financial data updates when companies file new reports.
Can I use my own LLM (Claude, ChatGPT)?
We provide secure access to the full analysis through your preferred AI interface. Ask questions, test assumptions, and explore the methodology — all grounded in real data, not generic training knowledge.

See the analysis. Understand the assumptions. Form your own view.

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