For agents & agentic teams
Built for the deal team — and its agents.
The people who model deals increasingly work alongside AI agents that read documents, draft memos, and pull data. Merger Waterfall is designed to be legible to those agents and — soon — callable by them, so the model at the center of the deal stays deterministic and auditable no matter who assembles the inputs.
The pattern
Context, compute, confirm.
Agents are good at gathering and drafting. The waterfall is exact math. People own the calls that matter. Merger Waterfall is built so each does the part it's best at.
Context
Agents read how the model works
An agent grounds itself in how the waterfall is actually computed — seniority, participation, caps, the conversion election — instead of guessing. The site is agent-legible today through llms.txt.
Compute
Agents call the engine
Hand Merger Waterfall the consideration, the bridge, and the cap table; get back a deterministic, fully-audited waterfall. A language model shouldn't do preference arithmetic — it should call something that does it the same way every time.
Confirm
Humans sign off on what matters
The inputs the deal turns on — the term-sheet reading, the caps, the escrow and earnout terms — are confirmed by a person before the numbers go out. Agents draft and assemble; people confirm the critical data. Every figure keeps its trail.
Why a tool, not a guess
The waterfall is math, not a judgment call.
Liquidation preferences, capped participation, and the as-converted election are exact, iterative calculations. Get one tier wrong and every number below it is wrong — quietly.
That's a poor fit for a model that predicts the next token, and a good fit for an engine that solves the stack the same way every time and shows its work. When an agent calls Merger Waterfall, the answer is reproducible, benchmarked against the real distribution logic, and traceable to the tier it came from — the same number a person would defend across the table.
What the agent gets back
- ▸ Deterministic per-class and per-holder proceeds
- ▸ MOIC per holder, and 0-or-more classes converting
- ▸ The full tier-by-tier audit behind every figure
- ▸ Excel and read-only-link exports of the same result
A realistic flow
One deal, an agent and its human, end to end.
The critical inputs stay human-confirmed. The exact math stays in the engine. The agent does the rest.
An agent reads the term sheet and draft cap table, and pulls out the consideration mix, the bridge items, and each class's terms.
It hands those inputs to Merger Waterfall's engine, which computes the waterfall deterministically — preferences, participation, caps, conversions — with a full audit trail.
A person confirms the figures the deal turns on: the preference multiples, the caps, the escrow and earnout terms.
The agent drafts the stakeholder summary and the board exhibit — every number traceable to its tier, and shareable as a read-only link.
On the $15.00M example, the engine returns $11.85M distributed across four classes with zero converting — the same result the how-it-works walkthrough shows a person building by hand.
Ways to plug in
Three surfaces, clearly labeled.
One is live now; two are in early access. We'd rather tell you exactly where each stands than imply more than ships today.
llms.txt
A plain-text map of the site, the methodology, and the canonical deal — for any agent that reads it.
Read /llms.txt →MCP server
Expose the waterfall engine as a tool your agents — Claude and others — can call directly. In development; join early access to shape it.
REST API
Programmatic access to the same engine and exports, on the roadmap alongside MCP.
The MCP server and API aren't live yet. We're building them with early deal teams — tell us how your agents would use Merger Waterfall and we'll bring you in early.
Building with agents?
Get early access to the MCP server and API, and help shape how agents call the waterfall.