A data capture solution is usually sold on how well it reads documents and is only half a solution at that point. Extraction produces values; validation decides whether they can be trusted. Without the second half, capture converts a slow reliable process into a fast one that occasionally produces confident errors nobody catches until much later.
The extraction half
Turning a document into fields, with a confidence value per field so that doubtful values can be routed to a person. Confidence exposure is the property that distinguishes serious products, because without it you are choosing between reviewing everything and reviewing nothing, and the second is how wrong data reaches a ledger quietly.
The validation half
Checking extracted values against what you already know. Does the supplier exist. Is this invoice number new for them. Do the lines sum to the net and does net plus tax equal the total. Is the date plausible. Do the bank details match. Cheap rules, run in milliseconds, catching a large share of both errors and fraud.
And the review path
Whatever survives validation with low confidence needs a person and a screen showing the document beside the field, with correction in one keystroke. This is where your team spends its time and it is the part least visible in a demonstration, which is a good reason to insist on seeing it with your own messy documents.
Questions people ask about data capture solution
Who builds the validation rules?
Usually you, against your own data. Some products ship defaults; the ones that matter most are specific to your suppliers and your ledger.
Should rules block or warn?
Duplicates and bank detail mismatches block. Arithmetic and plausibility warn, since they usually indicate extraction problems.
How do we measure a trial?
By the human minutes still needed per document afterwards, on your own worst inputs, rather than by an accuracy figure.