Evidence note, 5 min read
From 141 findings to 21 review items
Why a useful comparison system should reduce noise, not merely detect more differences.
In document reconciliation, raw detection volume is often mistaken for quality. The operator experiences the opposite: every low-value finding is another interruption.
Detection is only the first layer
Schedules and quotes describe the same project through different structures, naming conventions, and levels of detail. A literal comparison can produce a long list while still missing the business meaning.
The system must normalize aliases, quantities, models, context, and explainable variation before assigning a review state.
The benchmark
On one benchmark dataset, the reconciliation workflow reduced 141 raw findings to 21 actionable review items. This is a validated technical outcome on that dataset, not a universal accuracy or labor-savings claim.
The important result was concentration: expert attention moved toward the items with genuine ambiguity or commercial impact.
Evidence before automation
Each review item should show the schedule source, quote source, normalized interpretation, applied rule, and reason for escalation.
Once reviewers trust that trail, the system can learn from corrections and expand into adjacent order and pricing checks.