What is document intelligence?
Document intelligence turns information in files into something a team or system can use. It can recognise text, understand layout, extract named fields and flag values that need checking.
By Exemplary AI · Updated · Illustrated explainer
The key idea
Reading the words is one step. Knowing what they mean in the document is another.
The idea, illustrated.
From a page to usable information
Figure 05No. INV-1048
Issued: 04 Sep 2026
Due: 04 Oct 2026
Then compare with the purchase order and review exceptions.
- 01
Read the page
Extract text and its layout, including headings, tables and the relationship between labels and values.
- 02
Name the information
Map the right value to the right field: an invoice date is different from its payment due date.
- 03
Check before use
Validate formats and business rules, keep the source and send unclear results for review.
Why text alone is not enough
A page may contain several dates, repeated supplier names and totals in more than one currency. A text transcript preserves words, but it may lose which column a number belongs to. Document intelligence adds structure so downstream work can use the right value.
The Docling report describes separate models for page layout and table structure. That illustrates an important distinction: extracting characters and understanding how a page is organised are different problems. A neat output table does not prove that the original rows were read correctly.
Primary source: Auer et al.: Docling Technical Report
What makes an extracted value dependable?
The team needs a definition for each field. Does “total” include tax? Does “date” mean issue date or due date? If a field is missing, the output should preserve that absence rather than guess. Calculations and checks can then expose contradictions, such as line items that do not add up to the stated total.
The real document mix matters as much as the example used in a demonstration. OmniDocBench examines different document types and layout characteristics. Our practical interpretation is to include unfamiliar layouts, tables and difficult scans when deciding whether an extraction process is ready for work.
Primary source: Ouyang et al.: OmniDocBench: Benchmarking Diverse PDF Document Parsing
An everyday example
An invoice with two dates
An invoice says “Issued 4 September” and “Due 4 October.” A useful extraction returns both dates under their correct names, along with the invoice number, currency and amount.
If the scan makes the invoice number ambiguous, the result is held for review. A missing character should not silently become a new identifier in the finance system.
A common misconception
“If the page has been read, the data is ready to use.”
Recognition, structure and business validation are different checks. A correctly read number can still be assigned to the wrong field.
How Exemplary helps
We connect the files to the next step.
Exemplary’s File store brings uploaded documents, extracted text, search and source references together. We help configure the fields and workflow your team needs, with review where the task calls for it.
Explore File storeSources & further reading
Primary publications and documentation. Accessed 22 September 2026.
- 01Docling Technical Report
Auer et al. · Technical report · 2024
- 02OmniDocBench: Benchmarking Diverse PDF Document Parsing
Ouyang et al. · CVPR · 2025