An AI-first OCR pipeline, with people for the exceptions

Turning a manual document workflow into automated intake, classification, extraction and validation on Cloudflare Workers, with human review only where automation fails.

This project took a document workflow for vehicle dossiers that was done almost entirely by hand and made it automated by default.

Before

The core flow was manual:

receive images and documents;
classify them;
type in or extract the fields;
check the data;
prepare the submission.

Every step depended on a person, so every document waited for one.

After

I built the workflow end to end, along with the review tooling:

Intake from two places. Submissions arrive through a Zalo webhook, and staff can also create records in the admin.
Automatic classification of each document.
OCR field extraction.
Duplicate detection, so the same document doesn’t get processed twice.
Validation rules that check the extracted data.
Exception review for anything automation couldn’t handle.
Submission package generation at the end.

AI-first, with people for the exceptions

People don’t check every document before it moves on. They aren’t removed from the process either.

Documents are classified and extracted automatically. A person steps in only when automated processing fails or needs correcting. Human attention goes to the cases that need it, not to re-checking work that’s already right.

I’d describe it as AI-first with exception-based human review. It isn’t human-first, and it isn’t fully autonomous: the review step is where failed or wrong extractions get corrected.

The stack

Cloudflare Workers for the application and processing steps;
Queues to move documents through the stages;
D1 for structured data;
R2 for the images and files;
React for the admin and review tools.

Queues suit a staged pipeline like this one, where each document moves from intake through classification, extraction and validation.

What I can’t tell you

I have no published accuracy rate, throughput figure, document volume or time-saved number for this project. Without them, the honest claim is about the shape of the workflow: a manual pipeline became an automated one, with human review as the exception path. Any percentage I quoted would be one I made up.

The general idea is in the garden: Exception-based human review.