Challenge
Manual document processing is slow and error-prone
Many invoice workflows still depend on manually reading documents, copying supplier details, checking dates, entering totals, and keeping the original files organized for future reference.
Case Study
RB AutoAI
AI automation platform that streamlines invoice processing across WhatsApp and email using OCR, Gemini AI, and business workflow automation.
Role
- Frontend Development
- UX/UI
- AI Workflow Design
- Automation Architecture
- Deployment
Tech Stack
- Next.js
- TypeScript
- Tailwind
- n8n
- Gemini
- OCR
- Evolution API
- Google Drive
- Google Sheets
Overview
From manual invoice handling to structured automation
RB AutoAI was built to reduce the repetitive work involved in receiving, reading, storing, and organizing invoice data for small business and accounting workflows.
The system receives documents through WhatsApp or email, extracts invoice information with OCR and AI, stores the original file in Google Drive, and sends structured data to Google Sheets for review and export.
Solution
A practical AI workflow connected to tools businesses already use
Instead of forcing users into a new platform, RB AutoAI connects WhatsApp, email, Google Drive, Google Sheets, OCR, and AI into one automated document-processing flow.
Key Features
Built around real operational steps
Invoice capture through WhatsApp and email
OCR and AI-assisted data extraction
Original document storage in Google Drive
Structured invoice data exported to Google Sheets
Error handling for unreadable or incomplete documents
Conversion-focused landing page designed for lead generation
Architecture
Workflow architecture
WhatsApp / Email
OCR
AI Extraction
Google Drive
Google Sheets
Lessons Learned
What this project improved
This project strengthened my ability to design complete AI automation products—from conversion-focused landing pages to production-ready workflows integrating OCR, LLMs, cloud storage, and business automation. It also reinforced the importance of building systems around tools users already know, reducing adoption friction while improving operational efficiency.