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Manual document review doesn’t scale.
→ Extract critical fields from complex PDFs automatically
→ Turn unstructured documents into structured, usable data
→ Trace every important finding back to its exact page/source
→ Review hundreds of documents without hundreds of hours of manual work
This is what production Document Intelligence should actually solve.
#DocumentIntelligence #AIEngineering #GenerativeAI #RAG #IntelligentAutomation #DocumentAI #EnterpriseAI #LLM #AITransformation
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Creatives on Contra have earned over $150M and we are just getting started
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Document scanning is a technical process that happens in the background. Users have no way of knowing if it worked unless the interface tells them.
We designed the extraction animation to make the scanning and extraction sequence visible in real time, showing users the system actively working through their document.
The design choice was to use progressive visual feedback rather than a loading spinner. The goal is to build trust in an automated system by making it feel transparent.
Another successful knowledge management migration completed.
This project involved migrating approximately 200 pages across 48 Quip files into Notion before Quip's sunset. Since there isn't a reliable way to automate the migration, I manually exported and rebuilt the content while preserving structure, formatting, and hierarchy.
To make the workspace more valuable than the original, I used Notion AI to organize information, extract action items, clean up documentation, and transform scattered notes into a searchable, future-ready knowledge base.
A smooth migration with zero compromise on content quality and a much better workspace for the team going forward.
Impressive migration; preserving 200 pages while improving structure and searchability is a huge win for the team.
I engineered a custom desktop application for the ClubGG poker platform, focused on automation, real-time table interaction, and intelligent seat recognition.
The application dynamically detects active poker tables, identifies player positions, adapts to different table layouts (3-max, 5-max, and 6-max), and provides a responsive interface for interacting with the game environment in real time.
The project required combining frontend development, desktop application engineering, UI automation, DOM inspection, coordinate mapping, and performance optimization to create a smooth and highly responsive user experience.
What I Built
• Dynamic poker table detection
• Automatic seat recognition and positioning
• Multi-table layout support (3, 5 & 6 players)
• Responsive desktop interface
• Real-time game state interaction
• Coordinate & screen mapping system
• High-performance automation logic
• Clean, scalable application architecture
Technologies Used
Electron
JavaScript / TypeScript
Node.js
HTML5 & CSS3
Desktop Automation
DOM Inspection
Real-Time UI Rendering
Performance Optimization
Project Highlights
✔️ Automated table recognition
✔️ Intelligent seat positioning system
✔️ Multi-layout compatibility
✔️ Fast and responsive desktop application
✔️ Production-quality architecture
✔️ Optimized for reliability and scalability
This project demonstrates my expertise in desktop application development, automation engineering, browser technologies, and building complex software that interacts with real-time systems while maintaining speed, accuracy, and reliability.
GREAT JOB 👍