Implementation example
From document to record
Separate what AI reads, what it proposes and what the system saves.
The starting point
Reading the document gets confused with recording the expense.
The design objective
Extraction, review and recording stay distinct.
The decisions
behind the process.
Open the detail you want to explore.
Where it gets stuck
The same receipt arrives through another channel. An extracted amount looks right, but its project is unclear. If the workflow writes directly to the system, speed at the first step can multiply the cost of correcting the last.
The intervention
The document is received with its source. Extraction proposes fields, checks look for inconsistencies and possible duplicates, and the responsible person confirms the action that changes the system.
The decision that matters
An assistant’s response is not the same as a recorded expense. The workflow needs to distinguish what it read, what it proposes and what was actually saved. The team needs to see those differences.
What we check
A hard-to-read photo, a repeated document, an unidentified project and a failed write. The workflow should only say a record is complete after the destination system confirms the write.
Scope and limits of the example
Document automation depends on source quality and available integrations. This is not an autonomous product ready for every business; scope needs to be defined and tested for each process.
Let’s talk about the work