Most productivity consultancies measure your operation and hand you a report. I do the opposite end of the job: I find the manual work, then I remove it. Here's the path — start small, see the result, scale only if it's worth it.
You don't commit to a big project up front. We start with a free review and only go as far as the value justifies.
We look at one process that's costing your team time. I map how it runs today, find where the manual effort actually hides, and quantify the opportunity.
You leave with: a clear picture of what can be automated and a rough time/cost saving — free, no obligation.
We pick the single highest-impact bottleneck — a reconciliation, a report, a data upload — and I build the automation for it, using the systems you already have.
You leave with: a working, documented automation deployed in your environment, plus a walkthrough so the team owns it.
Once one piece is proven, we join the steps end to end — across SAP, Excel, Power BI and email — into a single process that runs on its own and flags only the exceptions.
You leave with: a finance process that's automated start to finish, consistent across the team, with a clear audit trail.
Three common situations and what changes once the manual work is removed.
Every month, someone matches bank, ledger and sub-ledger line by line across spreadsheets. Two days gone, and a difference is always found at the worst moment. Automated, the matching runs by amount, date and reference in minutes — only the genuine exceptions surface, already grouped by likely cause.
The management pack is rebuilt by hand each period: export, copy, paste, reformat — a full day, and always a version-control scare. Rebuilt as a self-refreshing report, it pulls straight from the source and updates on open. Same numbers, zero rebuild.
Invoices and journals are keyed into the ERP by hand — slow, error-prone, and double-checked just in case. Handled with scripted, validated uploads, the same entries post in one run, with a built-in check that flags anything outside the expected pattern before it reaches the books.
Representative examples of common finance-automation work — not specific client data. Actual results depend on your volumes, data quality and systems.
Knowing that a process wastes hours is useful — but you still have to fix it. I close that gap: the diagnosis and the build are the same engagement, so the time you identify as wasted is time you actually get back, not a recommendation sitting in a slide deck.
Tell me about one process that's eating your team's time. I'll tell you what can realistically be automated, which approach fits, and the likely effort — no commitment.
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