PoC: Building Intelligent Document Processing Solutions

ClickIT Case Studies PoC: How to Build IDP Solutions for Your Business

Proof of Concept

How to Build an Intelligent Document Processing Solutions That Adapt to Your Business

Solution:

IDP

Case study

In-House

In this PoC, ClickIT engineers turn a multi-week engineering task for new document types into a two-minute configuration update.

Organizations that handle numerous invoices, ID documents, contracts, and receipts often rely on rigid parsing scripts. Many modern companies use intelligent document processing solutions to prevent scripts from breaking when document formats change.

By shifting from hard-coded extraction logic to a schema-driven architecture, businesses can automatically classify, extract, and validate unstructured data from any file type.

Executive Summary: Key Takeaways

Why Traditional Document Extraction Fails to Scale

Organizations waste hundreds of hours on manual data entry from invoices, IDs, contracts, and receipts. Traditional extraction scripts require custom coding for every new document layout, creating massive technical debt.

The ClickIT Solution: A Schema-Driven AI Document Processing Architecture

We designed a POC t for Intelligent Document Processing (IDP) that ingests PDFs, scans, and image files and converts them into validated JSON payloads without requiring layout-specific parsing logic.

When an invoice, ID card, contract, or receipt is uploaded, the system automatically executes four steps:

Document Ingestion

Reads native digital PDFs or low-resolution smartphone photos

Automatic Classification

Detects the document category automatically with zero manual tags

Schema Enforcement

Extracts fields using Pydantic and JSON schema constraints to eliminate parsing errors.

Structured JSON Export

Delivers formatted payloads ready for ERPs, databases, or API integrations.

Supporting a new document format requires updating a central YAML file no redeployments, no new API endpoints, and zero code changes.

How It Works: Step-by-Step Intelligent Document Processing in Python

The system operates behind a FastAPI backend and a lightweight interface: users upload a document, inspect the live classification status, and review extracted attributes side by side with confidence scores.

Core System Capabilities

Zero-Touch Classification: Scans incoming content to identify document types (KYC IDs, invoices, receipts, contracts) before extraction starts.
Guaranteed JSON Outputs: Enforces strict structural output via Claude, eliminating malformed responses, broken strings, and unexpected missing fields.
Config-First Architecture: Mapping rules, detection keywords, and schema definitions live in a single YAML configuration file.
Local-First & Air-Gapped Ready: Runs locally without cloud dependencies, ensuring compliance with strict data security frameworks such as GDPR and HIPAA.
Lightweight OCR Engine: Leverages RapidOCR (ONNX) to extract text from images without heavy dependencies like PyTorch or complex system binaries
Automated End-to-End Testing: Backed by unit tests (pytest) and end-to-end browser automation (Playwright) testing file uploads, OCR accuracy, and output payloads.

Technical Stack Overview

Component Layer	Technology Selected	Architectural Purpose
Backend Framework	Python 3.11 + FastAPI	High-concurrency asynchronous API processing
Database & ORM	SQLite + SQLModel	Lightweight structured persistence
Digital Parsing	PyMuPDF	Embedded text layer extraction
OCR Processing	RapidOCR (ONNX) / Tesseract	Computer vision extraction for scans and photos
LLM Engine	Claude (Haiku 4.5)	Low-latency schema-constrained structured extraction
Classification	Rule & Keyword Engine	Deterministic document routing
User Interface	HTML5 + Tailwind CSS + JS	Zero-build real-time monitoring dashboard
Testing Suite	pytest + Playwright	Full unit, API, and visual E2E coverage

Why Claude Haiku 4.5?

High-volume data extraction prioritizes speed and cost efficiency over deep reasoning. Lightweight models execute structured extractions faster and more cost-effectively while maintaining accuracy.

Why Enterprises Need Modern Intelligent Document Processing Solutions

This architecture proves key design principles for scalable enterprise AI:

Extensibility Without Developer Overhead

Scale from 4 distinct document types (KYC ID, invoice, contract, receipt) to hundreds via configuration templates rather than per-type developer scripts.

Production-Ready Modularity

The architecture separates concerns (OCR engine, extraction engine, validation, storage) behind clean interfaces, allowing seamless infrastructure upgrades (swapping SQLite for PostgreSQL, local disk for AWS S3) without refactoring core logic.

Deterministic AI Pipeline

Using schema-guaranteed generation removes the volatility typically associated with LLM-generated text, eliminating one of the most common failure points in AI data pipelines.

Production Roadmap: Moving from MVP to Enterprise Scale

Capability Area	Local MVP	Enterprise Production Standard
Database	SQLite	Managed PostgreSQL / Aurora
File Storage	Local File System	AWS S3 / Cloud Storage
Pipeline Execution	Synchronous	Asynchronous Queues (Celery + Redis)
Security & Auth	Local Access	OpenID Connect (OIDC) / API Keys
Validation Layer	Confidence Scoring	Human-in-the-Loop (HITL) Interface