Forecasts that can be defended
Driver-based forecasting, scenario analysis, and decision models with visible assumptions. Built for finance leaders who have to explain the number, not just produce it.
- GL + drivers
- Assumption layer
- Forecast engine
- Validation suite
- Statements
- Audit trail
The problem
The forecast exists, but nobody can explain which assumptions drive it or defend it under questioning. Flattening a business into a single growth rate produces a number, not a decision tool.
What we build
- Driver-based forecasting and scenario engines
- Cash, balance-sheet, and project-economics models
- Statistical estimation where the data genuinely supports it
- Sensitivity analysis and management decision tools
- Validation suites that compare model output to source truth
- Balance and tie-out mechanics across entities and periods
Where the discipline comes from
Financial modeling imposes rules most software never faces: results must balance, tie to source, survive review, and remain explainable months later. Assumptions, drivers, and error ranges are shown beside the output rather than buried in a workbook.
Systems involved
- General-ledger and trial-balance data
- ERP and accounting platforms
- Excel where it is the right fit
- Power BI and management reporting layers
- Planning and operational driver data
Controls and validation
Drivers and assumptions are explicit and versioned. Backtesting compares the model against held-out history and simple baselines before any method earns its complexity. Validation ties output to source truth, and every scenario keeps the reasoning that produced it.
Implementation examples
- A driver-based balance-sheet and cash forecast across multiple entities
- Scenario comparison where management can see what moves the result
- A validation suite that ties forecast output back to the ledger
- A model-review surface showing assumptions, sensitivities, and error ranges
What this does not promise: A forecast is a decision tool with an error range, not an accounting fact. No model is presented as certain, and no output replaces management judgment.
Questions buyers ask
Do you use machine learning for forecasting?
Only when the data supports it. Driver-based accounting logic usually beats statistical extrapolation when the business knows things a model cannot see. A more complex method has to prove itself against a naive baseline first.
What happens when the forecast is wrong?
It is presented with its assumptions, sensitivities, and error range from the start. A forecast is a decision tool, not an accounting fact, and the model makes that visible rather than hiding it.
Can this work with our existing planning process?
Yes. The model is built around the drivers and reporting lines the business already uses, so the output lands inside the existing management process instead of beside it.
Who owns the model after handover?
Ownership is defined in writing for each engagement. Deliverables include the model, documented assumptions, validation reports, and the operating knowledge to run scenarios without the author present.