Turn spreadsheets into software
Turn spreadsheets
into software
Build Python-powered applications without leaving Excel. Keep workbook inputs and review in the grid, move substantial logic into a reactive Python notebook, and hand off a focused App experience when another person needs to operate the workbook.
Demand & Inventory PlannerApp-mode operator view · mixed-integer planningTry live demo →
Excel stays the interface. Python becomes the application layer.
Keep the familiar grid as the operating and review surface.
Maintain the multi-step Python program in one reactive application layer.
Hand off the same saved logic as a focused operating experience.
Keep the spreadsheet surface people already know. Put the multi-step program in one reactive notebook. When the workflow becomes repeatable, hand off the same saved logic as a focused App experience.
Used by professionals at organizations including…
Build in Edit mode. Hand off in App mode.
The workbook and saved Python logic stay the same. The presentation changes to match the person using it.
Connect inputs, write and test Python, inspect diagnostics, add controls and charts, and save the notebook with the workbook.
Change approved assumptions or controls and review the results without maintaining the implementation surface.
App mode is a presentation choice, not a permissions or source-protection boundary. The saved workbook still contains executable Python and should be reviewed and trusted accordingly.
Start with applications that are available now.
The public catalog remains intentionally focused while the application patterns are being tested. These examples are published finance and operations applications rather than placeholders for future coverage.
Cash-flow and liquidity planning
Forecast receipts and payments, apply visible management scenarios, and review ending cash, downside headroom, and liquidity-threshold warnings in the workbook.
Demand forecasting with backtests
Compare transparent forecast baselines with rolling-origin evaluation before publishing the selected forward plan and prediction bands back to Excel.
Volume-to-staffing planning
Forecast short-horizon workload, keep productivity and shrinkage assumptions visible in Excel, and translate demand into staffed-shift requirements.
Write the complex calculation once in Python. Call it from Excel.
Publish notebook functions and keep ordinary worksheet formulas as the interface your workbook users already understand.
BF.OUTPUT()
BF.FUNCTION()
def project_arr(revenue, growth):
return revenue * (1 + growth) ** 12
bf.publish(functions={"project_arr": project_arr})=BF.FUNCTION(“project_arr”, A1, B1)
Boardflare and Microsoft Python in Excel organize the program differently.
Python lives in worksheet calculations
PY capture: Python source and returned values remain part of Excel’s worksheet calculation model.
A natural fit when the Python calculation belongs directly in the worksheet cell model and native Microsoft integration is the priority.
Python lives in an application layer
A fit when code, explanation, intermediate results, diagnostics, controls, and reusable logic benefit from one coherent notebook beside Excel.
Neither model is universally better. The design question is where the Python program should live and what a workbook user should be expected to maintain. Compare the approaches in detail →
Runtime and trust still matter: Boardflare runs Python in the browser by default, authored code can make permitted external requests, and App mode is not a source-protection boundary. Read the security and data-flow model →
Try a real workbook application.
Open the inventory planner, change worksheet planning assumptions, and see the same workbook drive a reactive Python optimization model.