Cross-loop analytics that read from the same source as every Allometry module. Drill from a P&L line to a job to a crew to an asset to a contract — without a single warehouse-side join.
Board-grade KPI tiles. Annotated trends with the moves that mattered. Drill from board number to truck roll.
Revenue lives in Tableau. Ops lives in Looker. Asset performance lives in a vendor portal. The CFO asks 'why did Atlanta's margin collapse' and four teams pull four different reports that don't reconcile.
The six capabilities that make this module work end-to-end. Pick any one as your starting point — they compound.
28 dimensions across 4 loops in a single semantic model. Customer × asset × job × crew × region × period × scenario × policy. Drill anywhere.
From any aggregate — drill to source. From a P&L line to a job to a truck roll to a part to a contract clause. The path is always there.
Ask in plain English. The cube knows the schema, the metrics, the relationships. Answers come with the drill paths attached.
What-if a 6% wage increase? A vendor change? A pricing test? Modeled across the cube — revenue, ops, risk all updated together.
Quarterly reviews assemble themselves — the numbers, the why, the drill, the recommended action. Edited by humans; not built from scratch.
Embed reports inside Allometry workflows — the AE sees their own pipeline analytics inside Quoting Engine. Insight where work happens.
Question lands on the cube, semantic model resolves, answer renders, drill path attaches. From aggregate to truck roll in two clicks. Without ETL.
Natural language, or click-through from any module's chart. Same cube, same answer.
< 1sMetrics, dimensions, filters resolved. Joins implied by the model, not the SQL.
< 200msVisualization renders with drill paths attached at every aggregation level.
< 500msDrill from aggregate to job to crew to truck — without leaving the chart.
< 1s/levelMargin, NRR, gross retention, deal velocity — defined once in the semantic layer, queried everywhere. Disagreements about 'what counts as ARR' end on day one of implementation.
When the metric changes, every dashboard updates. Versioning preserves history. Auditable.
# Semantic Metrics · canonical definitions metric "net-revenue-retention": numerator = arr.end_period(cohort) denominator = arr.start_period(cohort) format = percent cohort = customers.fixed_at(period.start) metric "gross-margin": numerator = revenue.recognized - cost.cogs denominator = revenue.recognized # cogs definition is itself a metric cost.cogs = labor.actual + materials.consumed + freight.allocated + equipment.utilization metric "deal-velocity": expression = won.acv / cycle.days × 30 segment_by = [region, segment, ae]
Allometry's semantic layer mirrors out — your BI tools query the same cube.
Cube backed by your warehouse; no data leaves your environment.
Direct SQL access for analysts. Same metrics, same definitions.
Embeds into CRM, ERP, Allometry — insight at the point of decision.
Pick the question your team hates answering. We'll wire the cube, return the drill path, and run the QBR question in front of you live.