For finance & RevOps

Close faster. Defend everything.

Board memos should be reviewable, not mysterious. Arcus starts from approved warehouse data, cites the source context behind each answer, and helps finance teams turn recurring executive questions into traceable artifacts.

arcus.ai/memo/board-q3
02 · Revenue & margin

Example Q3 revenue memo with every figure reviewable.

Net new ARR
$4.82M
+18% vs plan
Gross margin
71.4%
+1.8pp QoQ
Net retention
112%
+3pp
CAC payback
14.2mo
+1.4mo

This mock memo shows the intended answer shape: a cited headline, the drivers behind it, and the raw evidence a reviewer can inspect before the memo is shared.

Arcus can support finance questions when revenue, cost, pipeline, and customer fields are modeled in the tenant semantic layer.

The goal is not to replace finance judgment. It is to make every board-facing claim easier to trace, challenge, and revise.

What changes when finance can just ask.

Illustrative outcomes for the workflows Arcus is designed to support. Public customer benchmarks will appear only after approval.

Less

Time-to-close, month-end

Variance research, journal explanations, and controller review should move faster when the source context is already attached.

Clearer

Forecast accuracy at 90 days

Scenario answers are easier to trust when every assumption and variance can be inspected.

100%

Of board figures cited to source

Every number in a reviewed memo should point back to the query, source context, or assumption that produced it.

Questions you'll ask in the first close.

For finance & RevOps
01

Walk me through the variance between actual and forecast revenue for September.

warehousevariance
02

Build the AR aging by customer segment and flag anything above the agreed threshold.

finance datareview
03

What does runway look like at 0%, +20%, and +40% headcount growth?

scenario modelassumptions
04

Net retention by cohort. Which months are dragging us below plan, and why?

cohortsource-backed
05

Draft the Q3 board memo from approved revenue, margin, NRR, and runway metrics.

memoreview
06

Headcount cost run-rate by department, and the variance against the Q4 plan.

finance datavariance
07

Show top customers by ARR with renewal context if those fields are modeled.

customer datawarehouse
08

Flag journal entries above a threshold that hit unusual accounts.

anomalyreview
Forecast that learns

Models that own their accuracy.

Arcus is designed to preserve the assumptions behind forecast work so finance can compare scenarios against actuals and explain what changed.

  • Assumptions visible. Forecast work should show the fields, windows, and thresholds used.
  • Variance review. Compare a scenario to actuals when the underlying data is available.
  • Memo-ready context. Keep the explanation beside the numbers so reviewers can challenge it.
Forecast scoreboard
Q1–Q3 · MAPE shown as accuracy bar
modellast forecastactualvarianceaccuracy
Net new ARR$4.10M$4.82M+17.6%
Gross margin70.2%71.4%+1.7%
Burn (monthly)$2.18M$2.34M+7.3%
Pipeline coverage3.4x3.1x−8.8%
Headcount cost$3.92M$3.94M+0.5%
Net retention109%112%+2.8pp
Data coverage

Starts with your warehouse.

BigQuerySnowflakePostgresFinance tablesRevenue tablesPipeline tablesCustomer tablesSemantic metricsScoped connectors by request
Finance pilots should prove trust before speed. This section describes the intended review loop: cite the figure, inspect the lineage, then decide whether the memo is ready.
AT
Arcus product teamFinance workflow note

The CFO's review layer.

Bring the warehouse-backed finance questions that slow the team down. Arcus will help scope the first pilot workflow.