finance · analytics

One revenue number everyone can agree on.

Billing says one thing, the CRM says another, and the board deck splits the difference. Your agent reconciles the sources case by case and returns a number with its provenance attached.

Why it works here

Revenue analytics, the Contextually way.

Cross-store truth

ARR, expansion, and contraction computed across billing, CRM, and product data in one program — not three exports stitched in a spreadsheet.

Any cut, on demand

By segment, by cohort, by rep, by geography — each question is a fresh program, not a request in the analytics backlog.

Provenance attached

Every figure links to the code and sources that produced it, so "where did this number come from" has a one-click answer.

The flow of data
snowflake.invoices_t12m 3,208,441 rows postgres.accounts 214,188 rows contextually runtime retention_rollup · NRR by segment provenance attached NRR table 734 tokens → context
The program, case by case
goal → net revenue retention by segment, trailing 12 months
inv = sql("snowflake", "SELECT account_id, amount, period FROM invoices_t12m") seg = sql("postgres", "SELECT account_id, segment FROM accounts") nrr = retention_rollup(join(inv, seg), by="segment", window="12m") return summarize(nrr, format="table", note_provenance=true)
scanned 3,208,441 invoice rows across 2 stores returned NRR by segment + provenance · 734 tokens → context
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See it on your data.

Book a demo and we'll run revenue analytics against systems like yours — your stores, your policies, live — and map the path to a deployment in your VPC.