Product / Scenarios
Scenario planning
Scenario work in Bridge Town is built around explicit assumptions and repeatable model runs. Your agent can branch a plan, adjust the drivers, run the model, and leave a reviewable history of what changed.
Get started Bring your own agent. Bridge Town does not run an LLM on your behalf.
Built for FP&A workflows
What scenario planning gives your team
Bridge Town is the versioned product layer beneath your AI agent: model storage, collaboration, run outputs, and review history that agents and finance teams can both understand.
Repeatable model runs tied to the assumptions and code that produced them.
Output comparison across versions instead of disconnected spreadsheet tabs.
Agent-readable project state so scenario changes can be planned before they are written.
How it works
Scenario workflow
Start from an existing forecast model and define the scenario question.
Let your agent branch the model and change the relevant assumptions.
Run the scenario and inspect outputs against the current plan.
Promote, revise, or discard the branch with the change history intact.
Common use cases
Common use cases
Upside and downside casesHiring-plan alternativesPricing sensitivityFundraising plans
Frequently asked questions
Product details
Can scenarios be compared later?
Yes. Scenario runs are tied to project versions, so teams can compare the assumptions and outputs that produced each case.
Does scenario planning require a first-party chat interface?
No. The product surface is MCP-native. Your agent works through Bridge Town MCP while the web app provides review, history, and output surfaces.
Connect your agent to Bridge Town
Start with a versioned workspace, then let your own MCP-compatible agent build, inspect, and revise the finance model with you.
Get started