Cross-invoice verification

How it works

Set the thresholds, and change once results are in.

Invoices connected

Contract digitization and data cleansing are not prerequisites.
Data analyzed

Every invoice is read, and each finding is traced to the place in the source document it came from.
Ranked by size

Findings are ranked by confidence and by value, so the largest problems come first.
Payments queried

Each flag is routed back to AP with its citation, ready to query before payment is released.

System architecture

Models must agree. Every figure must trace to source.

Multi-Model Consensus

Several independent AI models must agree before an answer is trusted. The system checks that they agree on the same facts, not just the same guess.
Source Tracing

Every figure is traced to the exact place in the source document. Where a source is missing, the item is marked incomplete rather than passed off as a confident answer.
Confidence & Portability

A confidence score of high, medium, or low sets how much review each item needs. The approach works with any model, without retraining, so the investment holds as models change.

Duplicate Invoices

Near-duplicates that arrive in a later period or against a different entity, bypassing simple ERP duplicate checks.
Impossible Billing
Insufficient Detail
Rate-Card Deviations
Cross-Invoice Patterns

proof: construction

The value arrived before any contracted work began.

$70k

Recovered in three months, roughly the output of one full-time person.

Processed: 15,000 Invoices
Identified: 6% Errors
Flagged: 871 Items
Cost Savings Y1: 12.4 Million USD