Error rate
88%
of large spreadsheets contain material errors.
Panko · 2008 review
Position · Why financial models belong in code
Code was always the better way to build financial models. The learning curve made it impossible. Until now.
LLMs translate fluently between English and Python in both directions. The builder's learning curve is gone. The reviewer's learning curve is gone. Your financial model can finally be built on code.
/ 01 · the unspoken truth
Every senior modeler has had the same 2am thought.
Forty tabs. A sign error you missed before the board meeting. A formula in one cell silently depending on a hardcode four sheets away. We've all thought there must be a better way. That just changed.
Case · 2012
$6.2B
JPMorgan London Whale, traced to a copy-paste in a VaR model.
Senate PSI report
Case · 2003
$24M
TransAlta — a bad sort, no version control, real-money trades.
10-K disclosure
Case · 2010
R&R
Reinhart-Rogoff. An Excel range error shaped global austerity policy.
Herndon et al.
/ 02 · why we never crossed over
Two learning curves. Both load-bearing. Neither resolved.
Code was always better on the dimensions that matter for serious modeling. But getting there required both sides of the workflow to be fluent in code. Builders weren't. Reviewers weren't. Stalemate.
What code gives you
What code gives you that spreadsheets can't deliver.
- Complete version history — every change visible, every revision retrievable.
- Modularity — last quarter's DCF is a function you import.
- Reproducibility — same inputs, same outputs, forever.
- Thousands of what-if scenarios in seconds. No add-ins.
- Composability — models that call other models.
Why we couldn't get there
Both sides had to be fluent. Neither was.
- Builders don't write Python. A year of part-time learning, minimum.
- Reviewers — CFOs, partners, IC members — can't audit code.
- A model nobody senior can audit is a model nobody senior approves.
- So the workflow stayed in the format the reviewer could read: the grid.
/ 03 · the compromise wave
Anaplan, Pigment, Causal — the best response possible before LLMs.
The third-generation FP&A platforms tried to solve the same problem. They invented proprietary formula languages because they couldn't expose actual code. The compromise inherited spreadsheet weaknesses and added a new learning curve — to a smaller language.
See how Bridge Town compares: Bridge Town vs Anaplan · Bridge Town vs Pigment
Dimension
Excel
Proprietary formula language
Code + AI bridge
Testing
None.
Limited primitives.
First-class. Automatic checks on every change.
Version control
Filename suffixes.
Snapshot only.
Real change history. Full version trail.
Scenario analysis
Slow or impossible.
Bounded by platform.
Native. Thousands of scenarios in seconds.
Reviewer access
Familiar grid.
New grid to learn.
Plain-English on hover.
Builder access
Familiar.
New language to learn.
English in, code out.
Implementation
Days.
Months.
Days.
Lock-in
Format lock-in.
Heavy.
Open. Plain text. Portable.
/ 04 · what changed
The LLM is the bridge. Both learning curves are gone at once.
The builder describes the model in English. Their connected AI generates clean, tested, idiomatic Python, stored in Bridge Town. The reviewer hovers any cell. Bridge Town surfaces the formula, where every number comes from, its assumptions, and the test that validates it. Both sides work in their native language.
Builder Analyst English
Bridge LLM translate
Foundation model.py tested · versioned
Bridge LLM explain
Reviewer CFO English
The same shift Cursor brought to engineers dbt brought to analysts Replit brought to everyone now hitting financial modeling.
/ 05 · the objection
"But Excel has Claude too." Why move?
Microsoft and Anthropic are putting LLMs inside Excel. That makes Excel a better Excel — not a better discipline. The things code gives you are properties of the model itself, not the editing experience.
Traceable, change by change
Your model history shows exactly what changed, line by line, across every revision. A spreadsheet doesn't. That history is the foundation of any real review and approval workflow.
Composable
Last year's LBO function is one import line into this year's deal model. Excel models can't import each other meaningfully.
Automatically tested
Automatic checks run on every change. The test coverage grows with your model — and catches errors before they reach the board.
Agent-ready
Autonomous finance agents work better with code than with cell references. The foundation that supports agents wins as agents become real.
Transparent
Code is plain text. Excel hides logic behind values. When a regulator or auditor asks what the model does, "show them the source" actually means something.
Open and portable
Your model lives in plain text, in version control you own, in a language used by millions of engineers. No vendor sits between you and the artifact.
/ 06 · what this unlocks
Things that have been impossible in Excel for thirty years.
Six capabilities, free the moment the foundation is code. Each one is the kind of thing a senior modeler has wanted for a decade and worked around for a decade.
Hero capability · the reviewer's side
Hover any cell. Plain English.
The reviewer never sees code unless they want to. Bridge Town surfaces the formula, its inputs, its assumptions, and links to the test that validates it.
Compare two versions
See exactly what changed between the model that went to the board and the one that came back.
Validate the WACC range
Assert that the cost of capital is between 4% and 15%. Catch the error before the model lands.
10,000 scenario paths
Real probabilistic analysis on a laptop, in seconds. No add-in. No slowness.
Review and approve the budget
Proposal workflow. Approvals. Comments on assumptions. The audit trail is automatic.
Reproduce last year
Open the v2024-Q4 snapshot. Run it. Get exactly the same numbers. Forever.
/ 07 · getting started
The migration is a non-event.
You don't have to learn Python. Your reviewers don't have to learn Python. Your counterparties never see the code at all.
1Describe it in English
Type what the model should do.
Your connected AI builds the model. Bridge Town stores it, runs the tests, and renders the grid view. You see what you've always seen — a model.
▸ "Build a 3-statement model. Q3 churn 4%."
→ model.py · 240 lines · 18 tests pass
2Import an Excel model
Upload your existing spreadsheet.
Your AI converts it into a clean, tested, versioned code model — every formula traced, every assumption in a full audit trail.
▸ upload q3-deal-model.xlsx
→ 412 cells migrated · 22 tests inferred
3Export back to Excel
Counterparties get a .xlsx.
The seller, the lender, the auditor, the lawyer — they receive a spreadsheet that looks exactly like what they expect.
▸ bt export --to xlsx
→ deal-model-2025-q3.xlsx · ready
/ 08 · build on the right foundation
The foundation of financial modeling is changing.
Bridge Town is a financial modeling platform where plans are stored as code — with a plain-English layer for everyone who doesn't need to read it.