Document AITutorial8 min

Create an AI document extraction pipeline

A tutorial for turning uploaded PDFs, forms, and documents into reviewable structured records.

Real example

Example: extract vendor onboarding documents

A company receives W-9 forms, insurance certificates, bank letters, and contracts from vendors. Operations needs a clean vendor record but documents arrive in inconsistent formats.

Upload files into a document table, parse text with page references, run structured extraction per document type, validate required fields, and create review tasks for missing or conflicting data.

Operations reviews exceptions instead of manually typing every field from every document.

Tutorial path

How to implement it

Step 01

Store the uploaded document with metadata, owner, source, and processing status.

Step 02

Extract text or chunks in a way that preserves page and section references.

Step 03

Ask the model for structured fields, missing evidence, and source references.

Step 04

Validate fields and create a review task for incomplete or conflicting outputs.

Step 05

Promote approved records into the main workflow and retain document provenance.

Checklist

Ready when these are true

Document ownership checked

Page references preserved

Structured extraction schema

Review task for failures

Approved records keep provenance

Field notes

What matters in practice

Extraction needs ingestion, parsing, model output, validation, and review as separate steps.

Document source and page references are as important as the extracted values.

Do not let failed extraction block the whole queue when a human can repair the record.

Avoid these mistakes

Common failure modes

Do not throw away page references after parsing.

Do not mix all document types into one giant schema.

Do not overwrite approved vendor data without reviewer consent.

Practical tip

Use document type as an early classifier. Specialized extraction schemas are easier to validate and review.

Apply this to a build