View On
Document Pro
DocumentPro is an AI platform designed to extract and structure data from highly complex documents without requiring custom model training. A single workflow allows organizations to extract information from PDFs, scanned files, emails, and cloud storage. This AI platform reduces manual work while helping teams maintain accurate data for better decision-making. DocumentPro transforms messy content into ready-made templates using OCR and smart language tools. It also lets users send files through Google Drive, email, or webhooks and processes them automatically. To support these capabilities, OCloud Solutions delivered a scalable AI platform using a serverless AWS infrastructure and Python 3.11. Our expert team also enabled multi-LLM support and implemented a flexible pay-as-you-go credit system to improve performance, scalability, and cost efficiency.
POPULAR FEATURES
Modern AI Workflows
Configurable workflows automate document import, parsing, processing, and export. This reduces manual work without requiring custom model training.
Multi-Model Extraction
Users can choose between Claude Sonnet 4.6 and GPT-4o for model flexibility, built-in reliability, and better cost control.
Smart Document Processing
The platform converts PDFs and scanned documents into structured templates using OCR and language models that accurately read messy content.
Seamless Integrations
Users can connect Google Drive, email, webhooks, and the Vector Store API to automate document collection and delivery.
PROJECT REQUIREMENT
OCloud Solutions modernized the DocumentPro platform by upgrading its runtime, adding new monetization, expanding AI support, and improving deployment and features.
- Move the entire platform off Python 3.9 onto Python 3.11 and refresh all outdated dependencies across the codebase.
- Build a credit top-up, pay-as-you-go purchase system that works alongside the existing subscription plans for added flexibility.
- Add Anthropic Claude Sonnet 4.6 as a selectable extraction provider, giving users a choice alongside the existing OpenAI models.
- Build a fully Terraform-based infrastructure-as-code for development and production, with automated continuous deployment.
- Improve email ingestion by including the sender in webhook payloads and filtering out inline attachments during processing.
- Integrate a Vector Store API into the platform's deployment to support richer document handling.
OUR APPROACH
We followed an automation-first development approach to deliver new capabilities quickly while maintaining quality, reliability, and consistent deployments.
Branch-Based Delivery
Every unit of work lived on its own feature branch and merged into production through carefully reviewed pull requests, ensuring controlled releases and code quality.
GitFlow Reviews
Every feature moved from development to production through reviewed pull requests to ensure code quality, traceability, and smooth releases.
Continuous Validation
Our team ran a deploy-test-fix loop in a dedicated development environment, which helped identify issues early and ensured production-ready releases.
Infrastructure as Code
Terraform and GitHub Actions automate infrastructure and deployments to keep development and production environments consistent.
Shared Architecture
Shared platform components supported new features, reducing development time and maintaining consistency across the solution.
PROJECT SUCCESS
The team overcame complex technical challenges and delivered every planned improvement, modernizing DocumentPro across performance, payments, AI, and reliability.
- Resolved a runtime migration that affected schema validation by refactoring legacy template extraction and pinning the correct validation library.
- Delivered a complete credit management system with Stripe payments, flexible credit packs, non-expiring balances, and automated transaction tracking.
- Integrated Claude with structured outputs, cost tracking, and an automatic GPT-4o fallback to improve AI reliability.
- Enhanced document processing through email ingestion improvements and Vector Store integration for richer knowledge retrieval.
- Improved platform stability with improved error handling, logging, and proactive failure notifications.
RESULTS
OCloud Solutions effectively transformed an AI document-processing platform into a more intuitive, adaptive, and scalable platform.
Enhanced security, compatibility, and long-term maintainability by upgrading the platform to Python 3.11.
Added a new revenue stream that lets users buy one-time credit packs, with credits that never expire and tiered discounts.
Support for both Claude Sonnet 4.6 and GPT-4o gives users more AI model choice, built-in redundancy, and better control over extraction quality and costs.
Automated deployments through Terraform and GitHub Actions replaced manual release processes, delivering faster, more reliable, and repeatable deployments.
Enhanced email ingestion and stronger error handling improved platform reliability and created a smoother document processing experience.
TECHNOLOGIES WE USE
