Multi-entity budgeting, automated rollups, driver-based forecasting and CFO-grade reporting — built on your data, with the version control and access model finance actually needs.
Most FP&A functions do not have a forecasting problem. They have a consolidation problem that only looks like a forecasting problem once the numbers disagree.
Each entity plans in its own workbook against its own chart-of-accounts mapping. Rollups are rebuilt by hand every cycle, and every rebuild is an opportunity for the numbers to diverge.
Intercompany eliminations, FX translation and allocations live in formulas nobody has re-derived in years. Close takes weeks because verification is manual.
Three versions of the plan exist and nobody can say which the board approved. Without versioning there is no defensible baseline to forecast against.
Sequenced so that each layer rests on one that is already trustworthy.
One chart-of-accounts mapping, versioned intercompany eliminations, FX translation and allocations — with lineage from every reported figure back to its source ledger entry.
Budgets built from the operational drivers that actually move them — headcount, volume, rate, mix — so a scenario is a change in assumptions, not a new workbook.
ML forecasts that learn from actuals rather than extrapolating last year, plus variance analysis that attributes a miss to its drivers instead of leaving an analyst to reconstruct it.
Board-ready packs generated from the governed model, and conversational access so a CFO can interrogate the plan without commissioning an analysis.
Driver-based forecasting trained on your actuals. Variance explanation attributed to drivers. Narrative generation for commentary that analysts currently write by hand. Anomaly detection across ledger entries before close, not after.
Autonomous planning agents that set targets without a human. Forecast accuracy claims quoted without a baseline. Any AI layer sold on top of a consolidation process that is not yet governed — it will produce confident, wrong numbers faster than the spreadsheet did.
Where a packaged platform — Anaplan, Pigment, Planful, Unit4 — fits your operating model, we implement and extend it. Where the planning logic is specific to your business, we build it on your data platform. We have no licence to defend either way.
A PE portfolio company taken from spreadsheet planning to a governed operating model with board-grade reporting.
Multi-entity budgeting and forecasting for a large pharmaceutical group, replacing manual consolidation across the plant network.
FP&A dashboards and covenant reporting for a multi-site provider network, generated from a governed model rather than assembled monthly.
Forecasting, scenario modelling and optimization behind the planning layer.
Explore →Lakehouse, MDM and quality gates — the governed ledger layer FP&A rests on.
Explore →Dynamics 365, Salesforce and NetSuite — quote-to-cash and revenue data feeding the plan.
Explore →No. We build FP&A systems rather than licensing a product. Where a packaged platform fits your operating model we implement and extend it; where the planning logic is specific to your business, we build it on your data platform.
Yes, but only after consolidation is governed. Forecasting across entities with inconsistent account mappings or unversioned eliminations produces confident, wrong numbers. The consolidation layer is the prerequisite, not a later phase.
Packs are generated from the governed model, so the figure in the board deck traces back to the ledger entry behind it. That traceability is what makes an AI-assisted number defensible in the room.
Tell us where your close breaks today — we will map what a governed planning model would change before you commit to a platform decision.