Value a Private Company

Add the FutureSearch connector if you haven't already. Then ask Claude:

Acme Industries is filing for IPO. Break it into its three business segments and forecast the fair market value of each in billions USD.

Go to futuresearch.ai/app and enter:

Acme Industries is filing for IPO. Break it into its three business segments and forecast the fair market value of each in billions USD. Then sum the parts and compare to the IPO price.

pip install futuresearch
export FUTURESEARCH_API_KEY=your_key_here  # Get one at futuresearch.ai/app/api-key

import asyncio
import pandas as pd
from futuresearch.ops import forecast

segments = pd.DataFrame([
    {
        "question": "What is the fair market equity value of Acme Consumer Hardware?",
        "background": "Annual hardware revenue ~$8B at ~25% gross margin. Mature product line.",
    },
    {
        "question": "What is the fair market equity value of Acme Cloud?",
        "background": "~$2B ARR, growing 60% YoY. Subscription / SaaS model.",
    },
    {
        "question": "What is the fair market equity value of Acme IP / Licensing?",
        "background": "~$400M annual royalty stream, weighted-average remaining patent life ~9 years.",
    },
])

async def main():
    result = await forecast(
        input=segments,
        forecast_type="numeric",
        output_field="fair_value",
        units="billions USD",
        context="Estimate standalone fair market equity value as of the planned IPO date.",
    )

    df = result.data
    sotp_median = df["fair_value_p50"].sum()
    cash_minus_debt = 1.5 - 4.0  # $1.5B cash, $4B debt
    equity_value = sotp_median + cash_minus_debt
    ipo_target = 60  # $60B target

    print(f"SOTP segment total: ${sotp_median:.0f}B")
    print(f"Equity value:       ${equity_value:.0f}B")
    print(f"IPO target:         ${ipo_target}B")
    print(f"Premium:            {(ipo_target / equity_value - 1) * 100:.0f}%")

asyncio.run(main())

The output adds {output_field}_p10 through {output_field}_p90 (float, monotonically non-decreasing), units, and rationale to each row.

Effort. Batches default to LOW effort (~$0.09 to $0.20 per segment). For a small number of segments where you want more accuracy per row, set effort_level="HIGH" for about $1.20 per row.

result = await forecast(
    input=segments,
    forecast_type="numeric",
    output_field="fair_value",
    units="billions USD",
    effort_level="HIGH",
)

A correlated-upside check: the sum of medians underestimates the price the market will pay if investors are simultaneously bullish on every segment. Take the 75th percentile across all segments and re-sum to see the optimistic case.

sotp_p75 = df["fair_value_p75"].sum() + cash_minus_debt
print(f"Optimistic (p75 across all segments): ${sotp_p75:.0f}B")