The question I hear most from business owners isn’t “how accurate is it?”. It’s “what happens when it gets something wrong?”
That’s the right question. The answer decides whether an automation gets used or quietly switched off after a month.
No model is right 100% of the time
Even very good document reading will sometimes meet a smudged photo, a field left blank or handwriting nobody could read. If the system guesses silently, one bad record can cost more than the automation saved that week. Once people find a silent mistake, they start checking everything by hand again, and the time savings are gone.
Make uncertainty visible
So every system I build has three parts.
- A confidence check on every field. The AI says how sure it is, and the system checks the value against simple rules: does the date make sense, does the ID number have the right format, does the total match the line items?
- A review screen for the exceptions. Anything that fails a check goes to a person, with the original document next to the extracted value. Confirming is one tap and fixing is one edit.
- A log of every correction. Each fix is a labelled example. Over time it shows exactly where the system struggles, which is where to improve it next.
In practice most documents go straight through, and people spend their time only on the few that need judgment.
It also makes adoption easier
A review screen changes how a team feels about automation. Nobody’s work disappears overnight. It turns into checking and deciding, which is the part that needs a person anyway. The people who knew the old process become the ones who make the new one better.
The rule of thumb
If a person would have checked it before, a person should be able to check it now, and only when it matters.
This is how the Agent Portal is designed to grow: AI fills the customer record from a photo of the paper sheet, and the agent confirms the two fields it couldn’t read. Homeo Casebook works the same way: the app ranks remedies and the doctor decides.