Intelligent document processing & AI workflows

Map the document journey before automating it

We follow a document from arrival to its final business action: email or upload, classification, data capture, validation, approval and entry into a system of record. The assessment identifies repeated work, exception types and the consequences of a wrong field. Sample material should represent the real mix of layouts, scans, languages and poor-quality inputs, with appropriate permission to use it.

Extract information with context and traceability

The pipeline may combine OCR, layout analysis, structured extraction and deterministic rules. We define a schema for the fields your process needs and preserve a link to the source page or passage for review. Missing information stays missing rather than being filled with a plausible guess. Greek and English content, tables, dates, amounts and business identifiers are tested against the actual document population.

Build validation and an exception queue

Extracting an amount is different from authorising a payment. The design checks required fields, arithmetic, duplicates and consistency with approved reference records where available. Uncertain or conflicting results are routed to a reviewer with enough context to make a decision. Review thresholds are calibrated on representative data; a model’s own confidence statement is not treated as proof of accuracy.

Connect to operational systems safely

Approved outputs can feed ERP, CRM, document management or a case workflow through controlled integrations. We define permissions, retry behaviour, duplicate prevention and an audit trail for changes. Sensitive actions such as creating a supplier, changing bank details or approving payment remain subject to the organisation’s verification and authorisation process. The system records both the extracted suggestion and the accepted value where needed for accountability.

Measure value after human review

A successful pilot is evaluated on field-level accuracy, exception rates, total processing time and review effort, not just the speed of the AI step. We compare the existing process with the proposed workflow, including handling of failures and corrections. The operating plan covers document retention, provider settings, access, monitoring and re-evaluation when layouts or business rules change.

Invoice, order and business-document intake

A document-processing project can begin with a defined stream such as supplier invoices, purchase orders or service requests. We identify the required fields, supporting pages and destination system. The design distinguishes extracting a value from deciding what that value means for a payment or obligation. Business rules and responsible reviewers determine whether the extracted record can move forward.

Review exceptions with the source in view

The reviewer should be able to compare an extracted field with its location in the original document. We define queues for missing pages, conflicting totals, unknown suppliers and ambiguous values, with an appropriate owner for each exception. Corrections need a record that can support quality analysis. This makes human review a designed part of the workflow instead of a hidden manual repair step.

Assess performance using your document mix

A pilot should include the scans, layouts, languages and quality problems found in actual incoming material. We evaluate field-level correctness and the amount of review required before a record is usable. Processing time is considered alongside duplicate handling and integration success. A change in document format should trigger a visible review rather than silently degrade the downstream data.

Common questions