---
title: "Document AI & Intelligent Document Processing"
canonical: https://wavect.io/services/document-ai/
language: en
description: "Extract, classify and validate invoices, forms and other documents. Wavect builds document workflows with source references, review queues and system integration."
image: "https://wavect.io/img/general/bak/open_graph_preview.jpg"
---

Document AI

# Turn documents into checked records your team can use.

A PDF arriving in a mailbox is often the start of another manual task. We build document processing that identifies the document, extracts the fields your workflow needs and sends uncertain results to a person before another system acts.

THE ENGAGEMENT

## From incoming file to an accepted record.

1. **Map the document** Formats, fields, variants and the business action each record triggers.
2. **Build the review path** Extraction, validation, confidence thresholds and a usable exception queue.
3. **Connect the handover** Write accepted records safely and record their source and status.

DIRECT ANSWER

Document AI converts agreed document types into structured, validated records and review tasks. The deliverable is a working intake-to-handover workflow, including extraction checks, exception handling and traceability to the source.

Best for

- Operations teams repeatedly copying fields from invoices, forms or supplier documents
- Products that need document intake with explicit human review

Consider another approach

- Questions and answers over an internal knowledge collection
- A reporting warehouse built from already structured records

## Choose the fields that change the next action

Start with one document family and the decision it feeds. An invoice needs amounts, tax fields and supplier identity; a service form needs a request type and routing information. We define required fields, allowed values and who resolves an exception. If colleagues mainly need answers from documents, start with an [Internal AI Assistant](/services/internal-ai-assistant/).

## Keep the original beside the extracted result

The workflow can combine OCR, layout-aware extraction, language models and deterministic checks. We choose the approach against representative samples, including poor scans and unfamiliar layouts. Each accepted field retains a source reference where feasible. Duplicate detection, safe retries and processing status prevent a repeated upload from creating a second business transaction.

## Give reviewers the information needed to decide

Confidence alone is insufficient for a payment or a contractual field. We define cross-field checks, required approvals and reasons a document must pause. Reviewers see the original, proposed values and failed checks together. Corrections become a controlled evaluation set; they do not silently retrain a model or change every future result.

## Illustrative acceptance: an invoice total does not reconcile

In an example test, the extracted line items disagree with the stated total. The workflow creates a review task and holds the export. After correction, the approved record reaches the target system once, with the source file and reviewer decision recorded. Acceptance checks both the correct record and the absence of an unintended duplicate.

## Deliver the process, including its exceptions

The engagement produces a field schema, sample-based evaluation, processing code, review workflow and handover notes. We agree document volume, processing latency, retention and access before implementation. Connecting accepted records to an ERP or CRM uses a bounded [API Integration](/services/api-integration/) scope. New document families are evaluated before they join the production flow.

## Questions before you start

### Can you work with scanned PDFs and photographs?

Yes, where the samples support useful extraction. We test rotation, blur, handwriting and layout variation separately and identify which inputs need recapture or manual handling.

### Do you guarantee every field will be correct?

We agree field-level acceptance criteria and a review policy. Critical fields can require independent checks or approval even when the model reports high confidence.

### Can the workflow run in our environment?

Deployment and model choices depend on your data-handling requirements, infrastructure and budget. We document the services that receive document content and agree retention before a pilot.

### What remains with our team?

You retain the agreed schema, workflow code, evaluation cases and operating instructions. A named owner handles exceptions and approves changes to the field definitions or destination systems.

## Continue with the right scope

- [Answers from internal knowledge](/services/internal-ai-assistant/)
- [Connect accepted records to your systems](/services/api-integration/)
- [Evaluate an existing AI workflow](/services/ai-evaluation-llmops/)

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