Nexora looks at this topic through the business process: what is happening now, where the loss occurs, how the solution is designed, and what result should be measured after implementation.
The topic
Most of a business's operational work still arrives in unstructured form: PDFs, scans, email, requests, contracts, forms, and attachments. An employee reads the document, looks for the needed fields, and moves the data into a CRM, an ERP, or a spreadsheet.
Microsoft describes document automation as a combination of orchestration, AI extraction, manual review, and data storage. That combination is what matters: AI extracts the information, the workflow decides the next step, and a person handles the exceptions.
UiPath cites the SCM Insurance Services case, where Document Understanding and RPA gave an estimated saving of about 80% of the time spent filing claims. AM/NS India reports more than 90% accuracy when processing vendor invoices and a cut of 6 to 9 days in the handling of each invoice.
How Nexora fits in
Nexora builds document workflow around a business object. For us a document does not end at OCR: after extraction the data has to be validated, written to the right system, and trigger the next process.
For example, an order from a PDF can create or update a customer, generate an order, check stock, create a task, and send a confirmation. A contract can be classified, linked to a deal, and sent for approval.
We choose AI or document processing only where the format truly needs intelligent extraction. If the data already arrives through an API or a structured form, there is no point in turning it back into a document.
How we see it
The main risk is trying to reach one hundred percent automation at any cost. Documents always contain exceptions: a different template, a handwritten note, a poor scan, a missing field, a new item.
That is why the architecture needs a confidence score, a manual review queue, a history of corrections, and the ability to use corrected data to further improve the model.
The second problem is downstream. Even perfect recognition has no effect if an employee then copies the result by hand from one system to another.
How we solve it
- We receive the document from email, a form, a folder, an API, or a portal.
- We classify the document type.
- We extract the required fields.
- We check confidence and business rules.
- We send exceptions to a review queue.
- We create or update a record in the CRM, ERP, or database.
- We trigger the next actions: an order, approval, notification, payment, or task.
- We log the document, the extracted data, and manual corrections.
What improvement to expect
Metrics: average document processing time, manual minutes, the percentage of straight-through processing, accuracy after review, backlog, and the number of downstream errors.
SCM Insurance Services estimates a cut of about 80% in the time to file a claim. AM/NS India reports more than 90% accuracy and a saving of 6 to 9 days per invoice. Komatsu Australia reached 99% accuracy on its trained sample and estimated hundreds of hours of annual savings.
Nexora's result has to be confirmed on the client's own type of documents. We first take a sample, calculate the baseline, and only then decide whether it is worth scaling the automation.
