Asset Layer for Reama AI
Automated document management system for renewable energy asset teams
Project Overview
I developed Asset Layer for Reama AI, an automated document management system specifically designed for renewable energy asset teams. This system leverages advanced machine learning capabilities to streamline document handling, deadline management, and presentation assistance for companies in the renewable energy sector.
The core feature I implemented is the Document Deadline Manager, an AI-powered system that analyzes documents and tracks deadlines, with intelligent extraction and confidence scoring for critical information. I also built the foundation for the upcoming Presentation Builder feature that will help create presentations from document content.
For the technical implementation, I used React and Tailwind CSS for the frontend, FastAPI for the backend, and integrated with LLaMA 3.1 through the Cerebrus API. The system stores data in PostgreSQL with pgvector for efficient vector searches of document content.
Key Features
Document Analysis
AI-powered extraction of key information from various document types
Deadline Tracking
Automated monitoring of important dates and deadlines
Confidence Scoring
Reliability metrics for extracted information
Multi-document Support
Processing capabilities for various document types in renewable energy
Technologies
Project Insights
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