Finance AI that actually earns its place in the close cycle.
Forecasting, anomaly detection, close automation and decision support - delivered by analytics specialists who understand finance controls.
Analytics that fit inside the finance calendar.
Finance teams do not need more dashboards. They need analytics that shorten the close, surface the outliers and improve forecast quality. Every analytics engagement we run is measured against finance KPIs - days to close, forecast accuracy, audit adjustments - not against analytics-delivery metrics.
Before work begins, we clarify the operating context, governance expectations, and commercial pressures behind the brief. That gives the engagement a clear purpose before technical analysis starts.
The result is a more complete advisory view: what matters now, where risk may surface next, and how recommendations can be implemented without creating unnecessary hand-offs or ambiguity.
Scope
Clarify the decision, deadline, stakeholders, and evidence standard before work begins.
Delivery
Combine partner judgement, technical review, and practical implementation planning in one workstream.
Follow-through
Convert findings into owners, actions, and next steps that leadership can track after the session.
Forecast automation
Machine-learning-augmented rolling forecasts with confidence intervals.
Anomaly detection
GL and sub-ledger anomaly detection running continuously, not just at year-end.
Close automation
Close-task assistants, reconciliation automation and commentary draft-assist.
Controls analytics
Continuous controls monitoring over journals, expenses and revenue recognition.
Variance analytics
Self-explaining variance analysis with plain-English commentary generation.
KPI explanation
Automated explanation of KPI movements back to the transactions that drove them.
Use-case selection
Shortlist by finance-value, data readiness and governance posture.
Data foundation
Data access, lineage and quality work that precedes modelling.
Model build
Model built on the client's own data, with explainability baked in.
Deploy & measure
Deployment into the close cycle with measured impact on finance KPIs.
Finance AI is only valuable if the CFO can name the hours, pounds or decisions it improved.
Analytics initiative stalling?
A two-week use-case review produces a value-ranked shortlist - with honest judgement on data readiness.