---
title: "Document intake and review: AI Use Case"
description: "Pull the fields out of incoming documents, check them against your rules, and route only the exceptions to a person."
url: https://lidvizion.ai/use-cases/document-intake-review/
site: Lid Vizion
index: https://lidvizion.ai/llms.txt
---

[Use cases](https://lidvizion.ai/use-cases/) · Operations

# Document intake and review

Pull the fields out of incoming documents, check them against your rules, and route only the exceptions to a person.

[Talk through this workflow](https://lidvizion.ai/contact/?need=ai-workflow&from=%2Fuse-cases%2Fdocument-intake-review%2F#write) · [Connect your tools](https://lidvizion.ai/services/integrations/)

Where it starts

## The trigger.

A form, contract or packet arrives by email, upload or portal.

How it runs today

- Someone opens every document and retypes the key fields.
- Missing signatures and mismatched values are found late.
- Nobody can say where a document is in the process.

The workflow

## Four bounded steps.

Each step has an owner and a stop rule.

1. 01 / Capture
   Documents land in one watched location.
2. 02 / Extract
   Fields are read into a structured record with a confidence for each.
3. 03 / Check
   Rules flag missing, inconsistent or out-of-range values.
4. 04 / Route
   Clean records move on; exceptions go to a named reviewer.

Where people stay in control

## Approval points.

- Reviewer approves every flagged record before it is filed.
- Low-confidence fields are always shown to a person.

How we would measure it

## Against a baseline.

Measured before and after, on the same work.

### Minutes per document, before and after.

### Share of documents needing review.

### Errors found after filing.

Related

## Go deeper.

- [Connect your tools](https://lidvizion.ai/services/integrations/): Put the right context within reach of the workflow.
- [Document Intelligence](https://lidvizion.ai/work/#document-intelligence): Structured data from real documents, with a review screen for the fields that need a human check.
- [What is computer vision?](https://lidvizion.ai/resources/basics/what-is-computer-vision/): Software that turns images and video into structured information a system can act on.
- [Automate document intake with human review](https://lidvizion.ai/guides/automate-document-intake-with-review/): A four-step playbook to capture incoming forms and packets in one place, extract the key fields, check them against your rules and send only the exceptions to a named reviewer.
- [How to extract data from documents with AI](https://lidvizion.ai/answers/how-to-extract-data-from-documents-with-ai/): How can a business use AI to pull information out of forms, PDFs and paperwork reliably?

More use cases

## Other places to start.

[All use cases](https://lidvizion.ai/use-cases/)

- [The weekly status brief](https://lidvizion.ai/use-cases/weekly-status-brief/): Operations Gather updates from approved tools, draft the brief, and flag the items that need a decision.
- [Inventory reorder alerts](https://lidvizion.ai/use-cases/inventory-reorder-alerts/): Operations Watch stock levels across systems, flag items likely to run short, and draft reorder suggestions for a buyer to approve.
- [Shift handover summaries](https://lidvizion.ai/use-cases/shift-handover-summaries/): Operations Pull the events, open issues and notes from a shift into one short handover summary, so the next shift starts knowing what happened, what is still open and what needs attention first.

An illustrative workflow pattern, not a result from a specific customer.

Bring the messy version

## One workflow. A better way to run it.

Show us the tools, the handoffs, and the part that keeps slowing your team down.

[Let’s map your workflow](https://lidvizion.ai/contact/) · [Read our approach](https://lidvizion.ai/approach/)