Full-Stack Development

AI-Powered Enterprise Knowledge Assistant with Real-Time Document Intelligence

Building an intelligent knowledge management platform that transforms how teams interact with organizational documents using generative AI.

The Problem

Scattered Knowledge, Slow Answers

Modern organizations struggle with fragmented knowledge bases spread across multiple platforms, file servers, and document repositories. Team members waste countless hours searching for the right information, leading to duplicated efforts, inconsistent responses to clients, and delayed decision-making.


The client needed a centralized platform where employees could instantly query their organizational documents using natural language and receive accurate, context-aware answers with proper source citations—all while maintaining strict access control and data privacy.

Challenges

Technical & Business Hurdles

  • Processing and extracting text from diverse document formats (PDF, Word, Excel, CSV, Markdown) with varying structures and layouts
  • Implementing efficient Retrieval-Augmented Generation (RAG) architecture to provide accurate, context-aware AI responses
  • Building real-time streaming responses for a ChatGPT-like user experience without blocking the UI
  • Designing a flexible role-based access control system supporting project-level permissions (Owner, Collaborator, Viewer)
  • Handling large document libraries without exceeding AI model token limits while maintaining response quality
  • Supporting dynamic URL-based sources with on-demand content fetching (Two-Phase RAG approach)
  • Ensuring each user's API key isolation for billing transparency and security compliance
My Role

Full-Stack Development & Architecture

  • Designed and implemented the complete system architecture from database schema to frontend components
  • Built the backend REST API using Laravel 11 with Sanctum authentication and middleware-based authorization
  • Developed the React 19 frontend with TypeScript, implementing a modern single-page application architecture
  • Integrated Google Gemini AI with custom prompt engineering for document-aware responses
  • Implemented Server-Sent Events (SSE) for real-time streaming of AI responses with partial save recovery
  • Created document processing pipelines for PDF, DOCX, XLSX, CSV, and plain text extraction
  • Built the Two-Phase RAG system for intelligent source selection and on-demand content retrieval
  • Designed and implemented the invitation system with email notifications for team collaboration
Features

Core Capabilities

AI-Powered Chat

Natural language queries with streaming responses, source citations, and conversation history management.

Multi-Format Documents

Upload and process PDF, Word, Excel, CSV, text files, and URL-based content sources.

Team Collaboration

Multi-project workspaces with role-based access, email invitations, and shared knowledge bases.

AI Briefings

Auto-generated project summaries tailored to each user's role and job function.

Export Options

Download conversations as JSON, Markdown, or professionally formatted PDF documents.

Secure & Private

User-specific API keys, encrypted tokens, and project-level access control for data isolation.

Business Impact

Measurable Results

70%
Faster Information Retrieval
5+
Document Formats Supported
3
Role-Based Access Levels
Real-time
Streaming AI Responses

  • Eliminated information silos by centralizing organizational knowledge into searchable project workspaces
  • Reduced onboarding time for new team members through AI-generated role-specific briefings
  • Improved decision-making speed with instant access to relevant document insights and citations
  • Enhanced collaboration through shared knowledge bases with granular permission controls
  • Provided cost transparency with per-user API key management and usage tracking
  • Enabled audit trails through conversation export functionality for compliance requirements
Architecture

System Design Overview

FRONTEND React 19 + TypeScript Vite + Tailwind CSS SPA Architecture SSE Streaming Client BACKEND API Laravel 11 + PHP 8.2 Sanctum Auth RESTful API + SSE Queue Workers AI SERVICES Google Gemini API RAG Pipeline Prompt Engineering Source Selection DATABASE MySQL / SQLite Users, Projects, Documents Conversations, Messages FILE STORAGE Local / S3 PDF, DOCX, XLSX CSV, TXT, MD DOC PROCESSING Parser Services Text Extraction URL Fetcher DATA FLOW: TWO-PHASE RAG ARCHITECTURE 1. User Query 2. Source Selection 3. Content Fetch 4. AI Generation 5. Stream
Tech Stack & Tools

Technologies Used

React 19 TypeScript Vite Tailwind CSS Lucide Icons Laravel 11 PHP 8.2+ Laravel Sanctum Server-Sent Events MySQL / SQLite Google Gemini AI RAG Architecture DomPDF Mammoth.js PDF.js XLSX Parser Vercel / Netlify


  • Frontend: React 19 with TypeScript, Vite bundler, Tailwind CSS for styling, Lucide React icons, SweetAlert2 for notifications
  • Backend: Laravel 11 with PHP 8.2+, Sanctum for API token authentication, middleware-based RBAC
  • AI Integration: Google Gemini PHP SDK, custom prompt engineering, Two-Phase RAG pipeline
  • Document Processing: PDF parsing (pdf-parser), Word documents (phpoffice/phpword), Excel (phpspreadsheet), URL content fetching
  • Real-time: Server-Sent Events for streaming responses with partial save recovery
  • Export: DomPDF for PDF generation, Markdown and JSON export formats
  • Deployment: Vercel/Netlify for frontend, traditional hosting or containerized backend