AI Document Validation

Case Study · Insurance · Document Intake

Eight hours to one hour
per client.

Makay spent up to eight hours on every new client requesting documents, reading them, working out what was missing and emailing back for the rest. Klevie built a platform that runs the whole intake loop on its own.

2 weeks
Insurance
Document automation
Document intake · per client
8h → 1h
to receive and validate one client's documentation
−87%
Time per client
2 weeks
Build time
One pass
Whole submission checked at once
Any format
Identity documents from any country
The problem

A job that ate a day
for every single client.

For each client, someone had to request the documentation, read through what came back, work out whether everything needed was actually there, and send an email asking for the rest. Done properly, that took up to eight hours per client.

It went wrong in a specific and expensive way. A person would spot two missing documents, ask for those, and miss that more were absent, that a document which did arrive held none of the required information, or that it was not the right document at all.

So the same client was asked two or three times, and each round added delay and friction at the worst possible moment of the relationship.

What kept going wrong

Up to eight hours of a person's time for every new client

Documents requested twice, because gaps surfaced one email at a time

Files that arrived without the information they were supposed to carry

The wrong document accepted, because nobody checked it field by field

Delay and friction on the client side, right at the start of the relationship

What Klevie built

The intake loop,
off a person's desk.

A client sends an email and it enters the system automatically. The system checks the submission against a defined set of required documents, and Klevie configures exactly which documents are needed and what each one has to contain. Then it emails the client asking only for what is genuinely still missing.

It is the same document-extraction pattern described in document data extraction with AI, delivered as part of the wider approach in AI systems for business operations. There is a working demo of the flow at insurance.klevie.com.

01
Email intake

The client's email lands directly in the system, with nobody copying attachments by hand.

02
Requirements configured, not remembered

Which documents are needed, and what each one has to contain, is set up per case instead of living in someone's head.

03
Reading for content, not layout

The system reads for what has to be extracted from each document, so an identity document from the United States and one from China are both handled.

04
One check across the whole submission

The full set is validated in a single pass, instead of gaps surfacing one email at a time.

05
Automatic request for what is missing

The client gets one email asking only for what is genuinely still needed.

06
A person on the exceptions

Anything the system flags goes to whoever owns the client relationship.

The result

A full day of careful work
became a short review.

Time per client
8h → 1h
document intake and validation
−87%
Reduction in hours
2 weeks
From brief to working platform
Quality
No repeats
of incomplete document requests
One pass
Validation per submission
Any country
Identity documents read
Scope
Insurance
and any document-heavy vertical
Configurable
Documents and required fields
Live demo
insurance.klevie.com
What did not change

The system does the checking.
A person stays accountable.

The system handles the verification and the routine request, and a person stays accountable for the client relationship and for the judgment calls the system flags.

As with everything Klevie builds, the automation takes the repetitive work and a person stays in the loop where judgment belongs. That is what makes it safe to hand the task over in the first place.

Where the line sits

The system reads every submission, checks it against the required set of documents and asks the client only for what is genuinely missing.

A person owns the client relationship and every exception the system flags for judgment.

Your business
could be next.
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