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Falcon LBO Analysis (falcon-comps.vercel.app)
x402
Falcon LBO Analysis (falcon-comps.vercel.app) is a paid API for AI agents from falcon-comps.vercel.app, paid per call via x402, $4.49/call, status unknown (last checked 2026-10-31).
Runs a full take-private leveraged buyout (LBO) model on a US public company from a ticker, returning entry valuation, debt structures, IRR/MOIC, sensitivity grids, DSCR, and a credit memo — all sourced from SEC filings.
Price / call$4.49
Success—
StatusUnknown
How this service works
falcon.sol: Use this when you need a quick take-private LBO on a US public company. Send a ticker (and optional peers); get entry EV with premium, sources and uses, 3 debt structures, a debt schedule with cash sweep, IRR and MOIC, a 3x3 IRR sensitivity grid, a Credit Stress downside case, per-year DSCR and warnings, exit at peer-median EV/EBITDA, and a credit memo with key risks, all built from SEC filings. Assumptions are explicit and overridable. $4.49 USDC.
Output
A JSON object containing: the target ticker, entry enterprise value with acquisition premium, sources and uses table, up to three levered debt structures (base, aggressive, conservative), a year-by-year debt schedule with cash sweep, IRR and MOIC per structure, a 3x3 IRR sensitivity grid across debt turns and exit multiples, a Credit Stress downside EBITDA scenario with annual DSCR and covenant warnings, exit valuation at peer-median EV/EBITDA, all modelling assumptions (explicit and overridable), and a credit memo with executive summary bullets and key risks list.
Example use cases
PE take-private IRR analysis
“Model a leveraged buyout of Dun & Bradstreet (DNB) with a 25% premium over current market price and peers like EFX and TRU — I need the entry EV, sources and uses, three debt structures, a cash sweep debt schedule, IRR, MOIC, and a 3x3 IRR sensitivity grid across exit multiples and debt turns.”
Credit stress test for LBO target
“Run an LBO on Bausch Health (BHC) and include the Credit Stress downside case with DSCR per year and any covenant warnings — I want to see how the deal holds up if EBITDA underperforms.”
Quick LBO credit memo for IC
“I need a full credit memo for a take-private of Envision Healthcare using its SEC filings — cover entry valuation, key risks, debt structure options, and exit at peer-median EV/EBITDA so I can present it to the investment committee.”
Technical details
Endpoint
POSThttps://falcon-comps.vercel.app/v1/lbo?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a rapid, data-driven LBO model for a US public company built directly from SEC filings — particularly when you want all the core PE deal outputs (entry EV, debt structures, IRR/MOIC, sensitivity grid, DSCR, credit memo) in a single call without manual spreadsheet work. Prefer it over generic financial data APIs when the use case is specifically leveraged buyout structuring, take-private feasibility, or credit underwriting for an acquisition.
Side effects
- Ticker not found or not a US public company — returns error with invalid ticker message
- SEC filing data unavailable or stale for the requested company
- Peer tickers not publicly traded US companies — peers ignored or error returned
- Insufficient EBITDA or revenue history to build a debt schedule — model may return partial results
- Payment of $4.49 USDC not confirmed — request rejected with payment required error
- Rate limiting or server timeout on complex multi-peer models
Output
Schema · Response
{
"type": "json",
"example": {
"memo": {
"summary": [
"..."
],
"key_risks": []
},
"ticker": "HCA",
"returns": [
{
"irr": 0.137,
"debt": 70515000000,
"moic": 1.9,
"entry_ev": 152000000000,
"structure": "base",
"sources_uses": {
"sources": {
"debt": 70515000000,
"sponsor_equity": 83000000000
}
},
"exit_multiple": 8.9,
"entry_multiple": 9.7
}
],
"assumptions": {
"premium": {
"v": 0.3,
"why": "Control premium"
}
},
"sensitivity_irr": {
"irr": [
[
0.08,
0.11,
0.13
],
[
0.1,
0.137,
0.16
],
[
0.12,
0.16,
0.19
]
],
"debt_turns": [
3.5,
4.5,
5.5
],
"exit_multiples": [
7.9,
8.9,
9.9
]
}
}
}Tags
x402
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