The board does not want to know that margin missed. They want to know why, which lever moved it, and whether it is structural or one-off. Helm traces every variance through the twin's value-driver model and returns a ranked, plain-language explanation - with full lineage to source.
A variance is rarely one number. It is a stack of offsetting effects - volume up, price down, a cost overrun masking an otherwise strong month. Helm decomposes the result into the drivers that actually explain it, separates the favourable from the adverse, and keeps each component tied to the model and the underlying data. Two worked examples follow: one at the financial P&L level, one at the operational driver level.
The Analyst takes a top-line financial result - WMC's Profit After Tax of $14.8M actual against a $6.4M budget, a +$8.4M favourable variance - and decomposes it into the P&L drivers pulling in each direction. The beat was real, but the waterfall shows it was held back: logistics and admin overruns quietly absorbed a chunk of the upside.
Here the variance sits inside a multiplicative revenue formula - volume × grade × price × compliance × FX - where the drivers interact and cannot simply be added up. Turnover by grade came in at $31.5M against a $24.3M budget, a +$7.2M favourable variance. The Analyst applies Shapley decomposition so each driver receives a fair share of the result.
Both explanations are produced the same way - the difference is only how the driver structure is wired in the twin. The Analyst reasons over the model, not over a table of numbers.
The question is resolved against the twin's financial and operational vocabulary. The Analyst identifies the metric, the entity and the period, then pulls actuals and budget straight from source - with lineage attached to every figure.
Variance is split through the twin's value-driver structure. Additive structures decompose directly; multiplicative or formula-based structures - where drivers interact - are decomposed with Shapley, so each driver gets a fair, defensible attribution.
Drivers are ranked by contribution and direction, a plain-language narrative is drafted, and the result is laid out as a waterfall and table. Every number stays traceable to source data and twin lineage - ready for board, audit and regulator scrutiny.
Anyone who runs a monthly business review and is tired of the diagnostics taking longer than the action items - and anyone who has had to explain a variance to the board without a defensible breakdown behind the number.
NoteBoth demo animations are extracted from live AI Analyst query sessions. Run-time has been compressed for display purposes — actual query durations typically range from one to three minutes. The data shown is simulated.
We'll show Helm decomposing a variance on a business that looks like yours - same value chain, similar reporting cadence, real numbers redacted as needed.
