I’m implementing an AI-driven automation setup for Eminence Technology to process documents such as invoices, forms, and contracts. The system needs to extract data accurately, handle variations in layouts, and manage exceptions like unreadable files or missing information. At the same time, I must ensure data privacy and comply with regulatory requirements. What tools, workflows, or validation methods can help improve accuracy rates, reduce manual intervention, and maintain consistent output quality in a real-world automation environment?
Loading
Sam HobbsPosted Dec 10, 2025, 10:13 PM
The first thing to do is to determine requirements. Determine what documents need to be processed. Determine what data in each document need to be processed. Are the variations in layouts predictable? If so then determine what each variation is. If the variations in layouts are not predictable then the work is more complicated, perhaps very complicated.
Using well-established system design methodologies you should not select any tools until you know what the tools will be needed for. You might not need AI to extract data accurately; don't decide to choose AI and then work on forcing the project to use it. As an employee of an AI technology company I hope your people guide others in the proper use of AI; to not use if when it is not needed.