Navigate any complex financial model in hours: M&A diligence, project finance stress-tests, client models you inherit. Every formula traceable. Every scenario exportable to a clean .xlsx your committee can review.
No credit card · .xlsx export always free · No AI training on your data
Whether it's a sponsor's LBO or a borrower's project finance model, the situation is the same: a file with too many tabs, assumptions buried in cells nobody documented, and a deadline that does not move. You paste fragments into Claude to gain ground, but the agent can't see the whole structure, you re-explain every session, and the data sits in a personal AI account.
15 tabs, 3,000 rows, variable names that made sense to whoever built it in 2019. You need to spot the key assumptions, identify where the leverage actually sits, and produce a view your MD can take to the investment committee, fast. Layerz reads the structure your agent can't see, so the analysis works on logic, not cells.
20 tabs, 6 debt tranches, DSCR computed somewhere in a column you have not found yet. Layerz maps debt sizing, DSCR logic and cover ratios, so your agent stress-tests revenue ramps, construction cost overruns, rate shocks against the actual financial structure, not against a scan of the grid.
| Today | With Layerz |
|---|---|
| Re-explain the model every AI session | Model structure persists: your agent knows the architecture |
| AI reads cells, not financial logic | Variables, formulas, and dependencies are machine-readable |
| Break-even, DSCR, cover ratios are manual recalcs | Run sensitivity on any assumption: leverage, revenue ramp, rate shock |
| Model audit is a manual trace through linked cells | Every formula traced to its source, defensible in front of an auditor |
| Building under deadline means recycling a 2019 file | Define the structure once, reinstantiate for each new deal |
| Upload client or project data to a personal AI account | Bring your own Claude: your account, your tokens. Layerz does no AI processing and trains nothing on your data |
| The committee and the counterparty need .xlsx | Export a clean standard file, no Layerz login needed on their end |
Drop the received Excel in your Claude session and tell it to map the model. Claude calls the Layerz MCP to extract the structure (debt tranches, DSCR logic, cover ratios, equity waterfall) into a typed backend it can navigate.
Ask Claude in plain English to find undocumented assumptions, run 5×5 sensitivity tables, or flag where cover ratios break. It works against the structure, not against a scan of the grid.
Tell Claude to export. Your IC, your MD, your co-lender opens a clean .xlsx. The structure persists across sessions, so you reopen tomorrow and continue.
map this LBO and find where leverage breaksrun a 5×5 sensitivity: DSCR × revenue rampfind every undocumented assumption in the debt scheduleexport the base case as a clean xlsx for the committeeRun these in any Claude session with the Layerz MCP installed.
Most transaction work is not building models from scratch. It is understanding a model someone else built, under assumptions that were in their interest to present favorably.
Layerz gives your AI the structural context to read that model intelligently, not just at surface level. When the sponsor's advisor says the base case DSCR is 1.35x, your agent already knows where the revenue growth assumption is, what the debt service schedule looks like, and what stress scenario makes it break.
That is a different quality of diligence than pasting tab by tab into a chat window.
On the build side, the wall is not the model itself: it is rebuilding it from a 2019 file every time the next deal lands. PPP, renewable, toll road, real estate development, LBO: same logic, slightly different shape.
Layerz lets you define the structure once and reinstantiate for each new deal. Every variable named, every formula traceable, every change logged. The model passes audit and the committee gets a clean export.
Define once. Reinstantiate per deal. Defensible end-to-end.
Fractional CFOs and consultants face the same wall as deal teams: rebuilding the same model for every new client. Build your standard model once, fork it per client, plug in each client's actuals, and send a clean .xlsx. When the engagement ends, the client keeps a live model they can open, audit and hand to the next person.
Layerz for fractional CFOs →LBO, PPP, infrastructure, M&A: financial architecture, debt tranches and DSCR logic, all mapped before you meet the team.
Leverage vs. revenue ramp. Construction cost overrun vs. rate shock. Clean output ready for the committee.
Circular references, undocumented assumptions, formula inconsistencies, flagged before the committee meets.
PPP, renewable energy, toll road, real estate, LBO. Define once, reinstantiate per deal.
Every variable named, every formula traceable, every change logged. Defensible end-to-end.
“In terms of crunching, it's actually faster than Claude.”
No credit card · .xlsx export always free · No AI training on your data