Document & reconciliation automation that saved a day a week

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.

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.