Flatland vs.
Datarails.
Datarails connects to your existing Excel workbooks and layers FP&A reporting, dashboards, and consolidation on top. Flatland sits underneath: a typed compile layer that authors the Excel from a deterministic IR. Different layer of the stack. Different audience for the output.
Datarails · founded 2015 · Excel-native FP&A platform for mid-market finance teams. · Flatland · 2026 · typed IR · MCP + REST · metered.
# Data flow:
# Your existing Excel models
# → uploaded / connected
# → Datarails ingestion
# → reporting / dashboards layer
Source: fy26_plan.xlsx
Sheets: 14
Cells: 47,883
Type system: Excel-implicit
Audit trail: Datarails change log
# Datarails reads it, exposes it, reports on it.
# The model itself remains an Excel artifact.Datarails treats Excel as the substrate of record. It connects to your workbooks, centralizes them, and layers FP&A primitives (consolidation, reporting, budgeting workflows) on top.
# Data flow:
# Your agent emits typed IR
# → Flatland compiles deterministically
# → typed bindings + assertions
# → Excel rendered from IR
IR: fy26_plan.ir.json
Drivers: 47 (typed)
Assertions: 12 (first-class)
Type system: Currency, Percentage, Count, …
Audit trail: git + bit-identical
# Excel is one rendering target.
# The model itself lives in IR.Flatland treats IR as the substrate of record. Excel is one downstream artifact: the deliverable. So is the IR JSON, so is the audit trail, so is the sensitivity result. All three deterministic outputs of one compile.
Both products end up looking at an Excel. Datarails treats the Excel as the source of truth and wraps it. Flatland treats typed IR as the source of truth and rendersthe Excel from it. If you already have a substantial Excel-based FP&A operation and want to layer reporting on it, Datarails is the right shape. If you're building the model from scratch with an AI agent, you want the substrate that's designed for that.
You're a mid-market finance team (20-500 people) with substantial existing Excel models that work, native ERP/CRM/HRIS connectors that matter, and a need for consolidation and budget-vs-actuals workflows out of the box. Datarails was built for that team shape and it shows. We don't try to be that.
Your model is being built, not consolidated. Your agent is the author. You want the substrate of record to be typed IR, not a workbook. You want pay-per-answer, not per seat.
Excel as the substrate.
Excel as the export.
Compile a model.
The Excel will exist after.
Three questions. A typed compile. A real six-sheet Excel export. The same workbook your auditor receives later.