Hours saved weekly
Match accuracy
Faster close
The challenge
Finance staff spent the better part of a day each week manually matching documents and reconciling figures.
Our approach
We built an extraction-and-matching pipeline with confidence scoring and human review for the edge cases that genuinely need it.
Expected impact
The weekly bottleneck became a brief review, accuracy improved, and the close cycle sped up across the board.
For this team, reconciliation was a standing weekly appointment nobody enjoyed: documents in every format, figures matched by hand, the better part of a day gone before the close could even start. The build did not try to remove the humans. It removed the re-keying, and left the judgement calls where they belong. Follow one document through the pipeline below.
The document-and-reconciliation pipeline
Follow one document from inbox to a closed book. Tap a stage to see what happens.
AI reads each document
Documents arrive in every format. An extraction model pulls the fields that matter (amount, date, party, reference) from each one, replacing the manual reading that used to swallow the better part of a day each week.
Confidence scoring is the trick
The reason automation can hit 99% match accuracy without a person checking everything is confidence scoring. Every field the model reads and every match it proposes carries a score. High-confidence matches pass straight through; only the genuinely uncertain items surface for review. That is how the hours come back without accuracy slipping, because people spend their time only where their judgement actually adds something.
How the pipeline is built
Documents are read by a document-AI and OCR layer, a Python matching service pairs each one with the right payment and ledger entry, confidence scoring routes the edge cases to a reviewer, and matched records post with a full audit trail, all running on GCP. The corrections a reviewer makes feed back into the model, so it gets steadily better at the messy cases. The weekly bottleneck became a brief review, the close ran twice as fast, and the finance team got its time back.
Time reclaimed on reconciliation
This build gave a finance team back 12 hours a week. Set your own weekly hours to estimate the gap for your team.
saved (delivered)
accuracy
close
You could reclaim
0 hrs
per week
0 hrs / year
The 12 hours a week saved, 99% match accuracy and 2× faster close are results delivered on this engagement. The estimate above is illustrative and depends on your own volume and data quality.
Built with
Frequently asked
- How did this save 12 hours a week?
- The old process meant a person reading documents and matching figures by hand for the better part of a day each week. The pipeline reads each document, matches records automatically, and only surfaces the genuine edge cases, so the weekly grind became a short review and the team got roughly 12 hours a week back.
- How is 99% match accuracy possible with automation?
- Because the system does not guess. Every extracted field and every match carries a confidence score, clean high-confidence matches pass straight through, and anything uncertain is routed to a person to confirm or correct. Those corrections feed back so the model keeps improving.
- Does automating reconciliation remove the finance team's control?
- No. The pipeline handles the repetitive matching so people stop re-keying, but a person still owns judgement and reviews the edge cases the system flags. The result is a faster close with a full audit trail, not a black box.
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