Build AI-Powered Document Intelligence Pipelines with Django & LLMs

Why Document Intelligence Matters in 2026 Every business deals with documents — invoices, contracts, resumes, forms, and reports. Manually extracting data from these documents is tedious, error-prone, and doesn't scale. With the rise of powerful LLMs and vision-capable AI models, we can now build intelligent document processing pipelines that automatically extract, classify, and analyze document data with remarkable accuracy. In this guide, we'll build a production-ready Document Intelligence Pipeline using Django for the backend and React for the frontend. By the end, you'll have a working system that uploads documents, extracts structured data using LLMs, and presents it in an intuitive dashboard. What Makes a Document Intelligence Pipeline? A modern document intelligence system typically consists of four layers: Document Ingestion — Upload and store documents (PDFs, images, Word files) Preprocessing — OCR, image enhancement, text extraction AI Processing — LLM-powered extraction, classification, and analysis Presentation — Dashboard and search interface for processed data Django excels at this architecture because its ORM, file handling, and REST framework give us a solid foundation for all four layers. Step 1: Setting Up the Django Document Model Let's start with a Django model that handles document storage and processing status: # models.py from django.db import models import uuid class Document(models.Model): STATUS_CHOICES = [ ('pending', 'Pending'), ('processing', 'Processing'), ('completed', 'Completed'), ('failed', 'Failed'), ] id = models.UUIDField(primary_key=True, default=uuid.uuid4, editable=False) title = models.CharField(max_length=255) file = models.FileField(upload_to='documents/') status = models.CharField(max_length=20, choices=STATUS_CHOICES, default='pending') extracted_data = models.JSONField(null=True, blank=True) document_type = models.CharField(max_length=100, blank=True) created_at = models.DateTimeField(auto_now_add=True) def __str__(self): return self.title This model gives us versioned document tracking with JSON-based extracted data storage — perfect for the semi-structured output from LLMs. Step 2: Building the AI Extraction Service Now let's create the core AI service that processes documents using LLMs. We'll use a service-based architecture to keep the logic clean and testable: # services/document_processor.py import base64 from openai import OpenAI from django.conf import settings client = OpenAI(api_key=settings.OPENAI_API_KEY) def extract_document_data(file_path: str) -> dict: """Extract structured data from a document using GPT-4o vision.""" with open(file_path, "rb") as f: base64_image = base64.b64encode(f.read()).decode("utf-8") response = client.chat.completions.create( model="gpt-4o", messages=[ { "role": "system", "content": ( "You are a document intelligence assistant. Extract all " "relevant structured data from the provided document. " "Return the output as valid JSON with keys: " "document_type, extracted_fields, summary, confidence_score" ), }, { "role": "user", "content": [ { "type": "image_url", "image_url": { "url": f"data:image/png;base64,{base64_image}" }, } ], }, ], response_format={"type": "json_object"}, ) return response.choices[0].message.content For PDF documents, we first convert pages to images using pdf2image , then process each page through the LLM. The result is always structured JSON that we can store directly in our model. Step 3: Creating the Django REST API Endpoints We'll expose two main endpoints — one for uploading documents and one for fetching processed results: # views.py from rest_framework import viewsets, status from rest_framework.decorators import action from rest_framework.response import Response from .models import Document from .serializers import DocumentSerializer from .services.document_processor import extract_document_data class DocumentViewSet(viewsets.ModelViewSet): queryset = Document.objects.all().order_by('-created_at') serializer_cla