AI Investment Report
Equity Investment Intelligence
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Claude agents × Python quant engine · Output benchmarked to Goldman IBD reports

Enter a company name, get an institutional-grade
equity investment feasibility report in 60 seconds

5 agents work in parallel — industry, financial, risk, valuation analysts and a report writer. Every number comes from real Python models (DCF / Monte Carlo / comparables / sensitivity); we never let the LLM make up numbers.

No signup required Delivered in 60s 100% reproducible numbers
16
complete sections
Covers the full IBD + DD flow
10,000
Monte Carlo runs
A well-grounded valuation distribution
60s
average run time
5 agents orchestrated in parallel
First 5
free forever
Readable with no signup
HOW IT WORKS

From form to finished report in 3 steps

Simple enough for anyone — no DCF knowledge needed; every field has an explanation and a default.

01

Fill in project info via a 5-step wizard

Company basics → industry & round → financial assumptions → competitors & team → submit. Don't know a number? Click 'Use industry defaults' to autofill.

About 2-3 minutes
02

5 agents generate in parallel

Orchestrator dispatches → Industry / Financial / Risk / Valuation run in 4 parallel tracks → ReportWriter integrates. Python models compute every number; Claude writes the professional narrative.

Streamed live
03

Read the full 16-section report instantly

The first 5 sections are free forever — executive summary, industry & market, business model, team assessment. Unlock the remaining 11: DCF, Monte Carlo, risk matrix, exit paths and more.

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REPORT STRUCTURE

16 complete sections · Benchmarked to a Goldman IBD pitch book

First 5 sections free forever · Unlock the last 11 for a one-time $1.90

01

Executive Summary

Executive Summary

One-line call + key numbers + three reasons

02

Project Overview

Project Overview

Company intro, product, milestones, funding history

03

Industry & Market

Industry & Market

TAM / SAM / SOM, growth drivers, macro trends

04

Business Model & Competition

Business Model & Competition

Revenue model, unit economics, Porter's Five Forces

05

Team Assessment

Team Assessment

Founder profile, team, key gaps

06

5-Year Financial Projection

5-Year Financial Projection

Full three-statement projection: income, balance sheet, cash flow

07

DCF Valuation

DCF Valuation

WACC derivation + discounted free cash flow + terminal value

08

Comparable Companies

Comparable Companies

EV/Revenue, EV/EBITDA, P/E multiples

09

Monte Carlo Simulation

Monte Carlo Simulation

10,000 runs, valuation distribution

10

Sensitivity Analysis

Sensitivity Analysis

WACC × terminal-growth two-factor heatmap

11

Risk Matrix

Risk Matrix

Quantified scoring across 6 risk types + mitigations

12

Term Sheet Suggestions

Term Sheet Suggestions

Valuation range, dilution, liquidation preference, anti-dilution

13

Exit Strategy

Exit Strategy

IPO / M&A / secondary probabilities and returns

14

Due Diligence Checklist

Due Diligence Checklist

Financial, legal, commercial, technical modules

15

Value Creation Plan

Value Creation Plan

Operating, commercial, organizational, exit levers

16

Appendix

Appendix

Assumptions, data sources, methodology, disclaimer

AGENT TEAM

6 agents = one IBD team

Each agent has its own system prompt and toolset, mimicking the division of labor in a real investment bank or fund.

Chief Orchestrator
Orchestrator
Duties: Decompose tasks, dispatch in parallel, integrate output

Decides which agents collaborate on each report, whether to run them concurrently or serially, and which Python tools each agent calls.

Industry Analyst
Industry Analyst
Duties: TAM/SAM/SOM sizing, industry trends, competitive landscape

Follows McKinsey + Bain industry methodology and the latest 3 years of public data to output market size and growth drivers.

Financial Analyst
Financial Analyst
Duties: Three-statement projections, unit economics, financial ratios

Calls financial_projection.py / ratios.py to output 5-year income, balance sheet, cash flow and key financial ratios.

Valuation Expert
Valuation Expert
Duties: DCF / comparables / Monte Carlo / sensitivity

Calls dcf.py + monte_carlo.py + sensitivity.py + comparables.py to give a triangulated valuation range.

Risk Analyst
Risk Analyst
Duties: Quantified scoring across 6 risk types + mitigation advice

Six dimensions — market, team, product, financial, regulatory, execution — each scored likelihood × impact, producing a risk matrix.

Report Writer
Report Writer
Duties: Integrate all output and write the professional narrative

Integrates the structured data from the 4 agents into a complete 16-section report, following IBD pitch-book writing standards.

QUANT METHODOLOGY

Every number is 100% reproducible

The LLM only handles prose; every number comes from open-source Python models — you can open thepython/directory and reproduce them line by line.

DCF discounted cash flow

EV = Σ FCFt/(1+WACC)^t + TV/(1+WACC)^n

A 5-year explicit forecast + perpetuity terminal value. WACC is derived via CAPM, including risk-free rate, beta, equity risk premium, debt ratio and tax shield.

python/dcf.py

Monte Carlo simulation (10,000 runs)

EV ~ f(g~N(μ,σ), m~N(μ,σ), WACC~N(μ,σ))

Gaussian sampling on three core variables — revenue growth, EBITDA margin and WACC — with 10,000 runs to get the P10/P25/P50/P75/P90 valuation distribution.

python/monte_carlo.py

Sensitivity analysis (two-factor heatmap)

EV = g(WACC, g_terminal)

A WACC 5×5 grid × terminal-growth 5×5 grid = 25 EV data points, so you can see at a glance which assumption the valuation is most sensitive to.

python/sensitivity.py

Comparable company valuation

EV = Median(EV/Revenue) × Revenue

EV/Revenue, EV/EBITDA and P/E multiples in parallel. Median multiple × company financials = implied valuation, triangulating the DCF result.

python/comparables.py
PRICING

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