Solutions — Bridge Town
Bridge Town is built for finance teams who want AI agents to do real work. Here’s how different teams use it.
FP&A teams at growth companies
The problem: Your planning tool is either a spreadsheet that breaks under version control or an enterprise product that costs six months to implement. Your AI agent can answer questions in chat — but can’t touch the actual model.
How Bridge Town helps: Your agent writes the model as Python, committed to a project with full version history. Every quarter close is a branch. Every assumption change is a diff. The EBITDA delta is visible before any change lands.
→ For FP&A and strategic finance
Investment banking and private equity
The problem: You build models fast, but each engagement starts from scratch. Version history is a filename convention (LBO_v4_FINAL_FINAL.xlsx). Due diligence means screenshotting cells.
How Bridge Town helps: Every LBO, DCF, or returns analysis is a versioned project. Branches for different scenarios. Runs against point-in-time data snapshots. Share a dashboard link, not a file.
→ For investment banking and private equity
Teams evaluating enterprise planning tools
The problem: Anaplan and Pigment are expensive, slow to implement, and lock your models into a proprietary formula language. You’re wondering if there’s a better path.
How Bridge Town helps: First model in minutes, not months. No proprietary language — your models are real Python. No vendor lock-in — .btx export means you own your work. Pricing scales with usage, not seat count.
→ Bridge Town vs Anaplan
→ Bridge Town vs Pigment
Teams integrating AI into finance workflows
The problem: Your team is using Claude or Codex for analysis but the outputs live in chat history, not in your models. You need a persistent, auditable layer where AI-generated work lands.
How Bridge Town helps: Bridge Town is the MCP server your agents connect to. They write to versioned projects. Every model, run, and data source is logged. Your agent’s work becomes part of your system of record.