Useful automation starts by understanding the real work, before choosing a tool. This guide helps you make that decision with data, tests and clear ownership.
Where it fits your workflow
We extract information from documents and send it to business systems with validation. We reduce manual data entry and keep an audit trail for exceptions.
How to plan the implementation
Identify documents, amounts and owners with clear rules. AI-extracted data should be checked against orders, suppliers or internal records before posting.
Before implementation, map the source data, the expected outcome and the person who approves exceptions. Check which systems offer an API and which steps need manual review.
What to measure and when to step in
Set a baseline and monitor minutes per document, discrepancies and pending approvals. An automatic outcome is not necessarily a correct one: keep uncertain cases visible and record staff corrections.
Check unusual amounts, duplicate documents and access permissions before closing the workflow.
Invoice data reconciliation
We compare invoice data between source and destination to find amounts, dates or references that do not match. The result highlights specific differences before the process closes.
See how we build it ↗What comes next?
Choose one workflow, document how it works today and note the time it takes and common errors. With that baseline, we can define a small test before expanding the automation.