Per-record sensitivity propagation: 4-arg typed DAG.
bench · commit 46a6328 →Financial models
as code.
Largest validated DAG. Compiled cold in under one second.
bench · scale-warm →Institutional Excel-export rubric. Three-pass compile, no drift.
rubric · six-sheet test →Same IR always produces the same outputs. Deterministic, recomputable.
determinism test →Both protocols, day one. Your agent calls the same engine your CFO can curl.
api reference →Then $0.10 per answer. No seat licenses. No proposal.
pricing detail →Not a spreadsheet.
Not a chatbot.
A typed compilation layer.
Spaceland · consumer hardware + subscription · fictitious demo model
Every answer
comes with its receipts.
=D7-D8 [ EBITDA = Gross_profit − Opex ]Live formulas via named ranges. Blue inputs, black formulas. Six sheets, no drift between them. The same artifact your auditor receives, generated from a typed compilation graph; not assembled by hand.
Why we still export to Excel →Your Series A model, compiled.
Describe your business in natural language. Out comes a typed model with named assumptions, named ranges, and an Excel your investor will read. Thirty minutes, not three weeks.
The compile layer the category runs on.
Compiled answers across your engagement roster. Typed drivers, recomputable models, audit-trailed exports. Built for the firm that bills as fast as it can defend the work.
A financial modeling MCP your agent can call.
Typed IR. DAG. Sensitivity. Scenarios. Excel. One npx command and your agent harness has a deterministic finance backend. No custom-tool glue, no JSON-pretending-to-be-a-model.
The typed substrate platforms embed.
If you're building a finance product on top of LLMs, Flatland is the deterministic layer underneath. We sit below your agent and behind your UI: IR in, audit-grade artifact out. You keep the customer; we hold the math.
Quantitative reasoning,
compiled.


