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AI document processing turns information in files into structured data that a business can use. A complete workflow can identify the document type, extract selected fields, check those values against business rules and send accepted records to another system. Unclear or conflicting information goes to a review step with the original document available.

OCR converts text in a scan or image into machine-readable text. Intelligent document processing adds tasks such as identifying document types, mapping text to fields, checking values and routing exceptions. OCR can be one component of that workflow. If searchable text is all you need, a full document-processing system may be unnecessary.

We scope workflows for invoices, purchase orders, application forms and selected information in contracts or other business records. Supported formats and document types are agreed after reviewing representative samples. Scans, handwriting, complex tables and unusual layouts need their own testing; we do not assume that one extraction approach will handle every file equally well.

We assess the accounting platform’s import options or API, the required fields and the permissions available. A workflow can prepare a draft record, attach the source document and route it for approval. Supplier matching, duplicate checks and purchase-order checks are defined separately. Extracting an invoice does not authorise payment or a change to supplier bank details.

We establish performance using representative documents and agreed expected answers before making an accuracy commitment. Results should be assessed by field and document type, including missing values and false matches. A model’s confidence score is not proof that a value is correct. Validation rules, source references and review decisions are part of the quality process.

The workflow marks the field as missing, uncertain or inconsistent and sends it to the appropriate review route. The reviewer should see the source page, the proposed value and the reason it was flagged. The system must not silently invent a value to complete a required field. Some cases need a corrected document from the sender rather than an internal guess.

We can scope extraction of selected dates, parties, references and clauses, with source passages available for checking. The field definition must make clear which date or obligation is intended. Contract interpretation and consequential decisions remain with the responsible reviewer; a summary or extracted clause is not a substitute for that review.

We agree which files may be processed, the providers involved, access permissions and retention requirements before implementation. The design covers storage, credentials, reviewer access and what is written to logs. Hosting and provider choices depend on your requirements. We do not assume that a general-purpose document service is appropriate for every sensitive dataset.

It can include checks using file identity and agreed business identifiers, such as supplier and invoice number. Those checks need rules for revised invoices, credit notes and legitimate repeated documents. A suspected duplicate can be held for review rather than discarded. We also design write operations so a retry does not create another record unnecessarily.

Implementation depends on document variety, fields, validation rules, review screens and destination integrations. Running costs can include document or page volume, OCR and model usage, storage, monitoring and maintenance. Review effort also matters. We separate the build quote from recurring assumptions and assess the workflow against the cost of an accepted, usable record.

Bring a representative set of documents you are authorised to share, including ordinary files and difficult examples. Include the fields you need, examples of correct outputs, approximate volumes and the system that should receive the records. Redacted samples can support an initial discussion, but the pilot must still test the variations that affect extraction and validation.