Project Overview
We developed an intelligent document processing platform that converts unstructured business documents into structured, usable data. The application automates document intake, information extraction, validation, human review, and data export through configurable workflows.
The platform is designed to help businesses process invoices, contracts, applications, financial statements, and other document-heavy records without relying entirely on manual data entry.
The Challenge
Businesses handling large volumes of documents spend considerable time manually reviewing files, extracting information, entering records into business systems, and checking data for errors.
This creates several operational challenges:
- Repetitive manual data entry across different document types.
- Inconsistent information extraction and formatting.
- Errors in financial amounts, dates, and reference numbers.
- Delays caused by manual verification and approval.
- Limited visibility into document processing status.
Our Engineering Approach
We designed a modular document processing architecture with dedicated extraction, validation, and review stages.
- Implemented multi-file uploads supporting PDF, DOCX, JPG, and PNG formats.
- Developed document parsing and OCR pipelines to extract text from digital and scanned files.
- Integrated GPT-4 with structured extraction prompts to convert document content into JSON-based records.
- Used Pydantic schemas to validate extracted fields, required values, and data types.
- Implemented background processing using Celery and Redis for asynchronous document handling.
- Designed configurable validation rules for financial totals, dates, currencies, and mandatory fields.
- Added role-based permissions, reviewer assignments, correction history, and audit logs.
Key Features Delivered
AI Document Extraction
Extracts vendor details, invoice numbers, dates, tax amounts, totals, line items, customer information, and other document-specific fields.
Intelligent Document Processing
Handles multiple file formats, including scanned documents, through text extraction and OCR workflows.
Configurable Workflow Builder
Allows administrators to define document types, extraction fields, validation rules, reviewer assignments, and output formats.
Human Review & Approval
Displays original documents alongside extracted information, allowing reviewers to correct values, add comments, and approve or reject records.
Batch Processing
Processes multiple documents through asynchronous queues with individual processing statuses and error handling.
Analytics Dashboard
Tracks document volume, processing time, extraction accuracy, reviewer corrections, approval rates, and processing failures.
Integration & Export
Supports structured CSV exports, REST API access, and integration workflows for external storage and business applications.
Business Impact
- Reduced repetitive manual document handling through automated extraction.
- Streamlined information verification and approval processes.
- Improved visibility into document processing and review activities.
- Centralized document records and extracted business information.
- Created reusable workflows for different document types and business operations.
Technology Stack
- Frontend
- Next.js, TypeScript, Tailwind CSS
- Backend
- Python, FastAPI
- AI & LLM
- OpenAI API, LangChain, Pydantic
- Document Processing
- PyMuPDF, AWS Textract, python-docx
- Database
- PostgreSQL, SQLAlchemy, pgvector
- Background Jobs
- Celery, Redis
- Authentication
- AWS Cognito
- Storage & Infrastructure
- AWS S3, Docker, AWS ECS
- Testing & Monitoring
- Pytest, Sentry, CloudWatch
