Case study deck · 9 slides
SaaS & finance
4 SaaS companies, $2M combined MRR
The challenge
Four companies, three accounting stacks, the same pain: a controller losing most of a week reconciling billing against cash that landed on a different clock. A mid-cycle upgrade booked revenue on the 14th; the settlement hit the bank feed on the 16th, net of a fee. Each controller had built brittle VLOOKUPs to paper over it, and each month something new broke the match.
The approach
We mapped the close calendar as it actually ran, not as the handbook said, and found the same four-step skeleton under all four processes: pull billing, pull the bank feed, match against the chart of accounts, render a close pack. We built one pipeline with swappable connectors and proved it first against the messiest stack, a manual multi-entity QuickBooks consolidation, before rolling it out to the cleaner setups.
What we built
We redesigned the workflow around the model, then built the pieces that make it run every day.
Connectors pulling billing, bank feed and GL data into one schema
Matching engine handling proration, settlement lag and fee netting
Exception queue surfacing only what the engine can't auto-match
Self-rendering close pack with variance analysis pre-populated
Inside the build
One reconciliation and close-pack architecture running under three accounting stacks, connectors swapped at the edges, matching logic held constant.
Tools & systems
01 · Source systems
3 accounting stacks, 4 companies
02 · ETL / reconciliation layer
150+ transactions matched monthly
03 · Close reporting layer
1-hour render time
Results
Down from 2–3 days, with ETL automation
Reduction in reconciliation errors
Transactions automated monthly
Of the freed time redirected to variance analysis and insights
How it stuck
The exception queue is why controllers kept opening the tool instead of quietly rebuilding spreadsheets on the side. Unmatched items dropped from 30-40 to single digits by month three, and every one left was a real judgment call, not a timing artifact. Two controllers now close mid-month on partial data just to watch variances trend.
“I used to block out the first week of every month. Now I close on the 2nd and spend the rest of the week actually looking at the numbers.”
Controller, portfolio company (4-entity SaaS group)
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