How is IDP different from OCR?

OCR extracts text. IDP understands the document: what it is, whether it is valid, and what should happen next. Most of our deployments combine both.

What document volumes justify IDP?

Teams processing hundreds of documents per month usually see clear ROI. The assessment quantifies it for your volumes before you commit.

Does it handle Saudi government forms?

Yes. We build handling for the specific document types your organization processes, including Arabic government correspondence and forms.

How long before it handles a new document type?

It depends on how different the type is and how much sample material exists. The practical approach is to onboard one type, prove it in production, and use that measured pattern to plan the next.

Will people still review documents?

Yes, by design. Confident cases complete automatically and uncertain ones go to a reviewer. That boundary is set with you and can be tightened as measured accuracy improves.

What if the extracted data is wrong?

Validation against your own records catches most errors before anything is written. Where a reviewer corrects a field, that correction is captured and used to improve subsequent accuracy.

Intelligent Document Processing (IDP)

Intelligent document processing goes beyond OCR: it reads a document the way a trained clerk would, understands what it is, validates it against your records, and takes the next step. Business Codes delivers IDP for enterprises across Saudi Arabia from its base in Riyadh.

In short

Document understanding combines OCR with AI so software does more than read a document: it identifies the document type, extracts the specific fields that matter, validates them against your records, and decides what should happen next.

Intelligent document processing: classify, understand, validate, then decide and route.

A good fit when

Documents arrive in many formats from many senders and no fixed template can cover them.
The same document type must be classified, extracted, checked, and then acted on automatically.
Field values need validating against your own records before they are trusted.
You want exceptions surfaced deliberately instead of discovered at month end.

Not the right fit when

Every document follows one rigid template — simpler template extraction will be cheaper to run.
You only need searchable text, in which case OCR alone is sufficient.
There is no reference data to validate against, which removes most of the accuracy benefit.
The process has no defined next action, so extraction produces data nobody consumes.

Not sure this is the right fit for your process?

Tell us what the process looks like and we will say plainly whether automation is worth it — including when it is not.

Ask our Riyadh team Estimate what this could save See the automation glossary

We reply within one business day.

What we automate with Intelligent Document Processing

Classification of incoming documents by type, department, and priority
Cross-checking invoices against purchase orders and contracts
Extraction of clauses, obligations, and dates from legal documents
Automatic routing to the right approver with the data pre-verified

What you gain

Touchless processing

The majority of routine documents complete without a person opening them.

Fewer disputes

Validation against your records catches mismatches before they become problems.

Faster cycle times

Documents stop waiting in inboxes; work starts the moment they arrive.

How it compares

Stage	Manual review	Template extraction	Document understanding
New sender or layout	Handled by a person	Fails until reconfigured	Handled
Field validation	Manual checking	Rule-based	Automatic against records
Decides next action	Person decides	No	Yes, within your rules
Effort to add a type	None, but slow	New template each time	Onboarding, then reused
Best suited to	Very low volume	Stable, identical forms	Mixed, real-world documents

Template extraction works while documents never change. Document understanding is for the mix that real organizations actually receive.

How we implement it

Classify the document mix
Identify which types arrive, in what volume, and which carry the most manual effort.
Define fields and rules
Agree the fields that matter per type and the checks each must pass to be accepted.
Connect reference data
Wire in the records used for validation, such as purchase orders, vendors, or employee data.
Build extraction and routing
Implement classification, extraction, validation, and the decision on what happens next.
Set the review threshold
Decide the confidence level below which a person checks, and staff the queue accordingly.
Measure and expand
Track straight-through rate by document type, then add the next type once the first is stable.

Limitations to plan for

Confidence is not certainty; a review path for uncertain extractions is part of the design.
New document types need onboarding — accuracy on an unseen format is not automatic.
Validation is only as good as the reference data it checks against.
Very poor scans limit what any amount of downstream intelligence can recover.

Common mistakes

Treating it as an OCR upgrade rather than a change in how the process is designed.
Automating extraction while leaving the approval process untouched, so the bottleneck simply moves.
Not capturing reviewer corrections, which discards the most useful accuracy signal you have.
Rolling out across every document type at once instead of proving one.

Security and governance

Documents are processed inside your environment and written to your systems of record.
Field-level extraction keeps a reference back to the source document and page for audit.
Review queues are permissioned, so only authorized staff see sensitive document types.
Automated decisions are logged with the confidence that produced them, making review meaningful.

Intelligent Document Processing FAQs

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