Document AITutorial8 min

Create an AI document extraction pipeline

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

Real workflow example

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.

Implementation approach

This guide is anchored in OpenAI structured outputs guide. Use the official API behavior as the boundary, then design the surrounding product state so the feature can be reviewed, retried, and improved.

Store the uploaded document with metadata, owner, source, and processing status.
Extract text or chunks in a way that preserves page and section references.
Ask the model for structured fields, missing evidence, and source references.
Validate fields and create a review task for incomplete or conflicting outputs.
Promote approved records into the main workflow and retain document provenance.

Code or config snippet when useful

type ai_document_extraction_pipeline_workflow_state = {
  sourceId: string;
  status: "draft" | "needs_review" | "approved" | "blocked";
  evidence: Array<{ source: string; summary: string }>;
  nextAction: string;
};

Field notes

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.

Mistakes to avoid

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.

Ready checklist

Document ownership checked
Page references preserved
Structured extraction schema
Review task for failures
Approved records keep provenance

Practical tip

Practical note

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

Use this as an implementation constraint, not just advice. The interface, server code, and validation path should make the same behavior true.

Apply this to a build