Docubox

Overview

Docubox is an AI-powered document management and knowledge platform built with Next.js, TypeScript, Firebase, OpenAI, Google Gemini, and Anthropic Claude.

The platform transforms uploaded documents into searchable knowledge through document parsing, Retrieval Augmented Generation (RAG), semantic search, natural-language Q&A, summarization, and multi-model AI orchestration.

Built With: Next.js, React, TypeScript, Tailwind CSS, Firebase, Firestore, Firebase Functions, Cloud Storage, OpenAI API, Google Gemini, and Anthropic Claude.

Multi-Model AI Orchestration with Task-Based Routing, Provider Fallback, and Cost Tracking.

RAG-Powered Document Intelligence with Semantic Search, Summarization, and Natural-Language Q&A.

Core Tech: Next.js | React | TypeScript | Tailwind CSS | Firebase | Firestore | OpenAI API | Google Gemini | Anthropic Claude

Button: View Live Project

Problem Statement

1. Difficulties Managing and Understanding Long Documents

Users need to extract useful information from uploaded documents without manually reviewing extensive pages of content.

2. Limited Search Across Document Content

Traditional keyword-based searches can make it difficult to find relevant passages when users ask questions using different wording.

3. Dependency on Individual AI Providers

Different AI providers have different context limits, costs, and output capabilities, making flexible model selection and provider fallback important.

4. Document Security and AI Response Delays

Uploaded documents require controlled access, while lengthy AI-generated answers can affect the responsiveness of document Q&A and summarization workflows.

Solution Statement

1. RAG-Based Document Intelligence

Built a document-processing pipeline covering text extraction, content normalization, chunking, vector embeddings, semantic retrieval, context selection, question answering, and summarization.

2. Semantic Search and Natural-Language Q&A

Implemented meaning-based document retrieval so users can ask questions about uploaded files and retrieve relevant passages without relying solely on exact keyword matches.

3. Multi-Model AI Orchestration

Developed a provider-agnostic AI layer across OpenAI, Gemini, and Claude with task-specific routing, context-aware model selection, fallback handling, streaming responses, token tracking, and cost monitoring.

4. Secure Document Storage and Streaming Responses

Integrated Firebase Authentication, Firestore, Cloud Storage, and Firebase Functions. Implemented user- and document-level security rules and streaming AI responses in the Next.js interface.

Features

• AI-powered document management and knowledge retrieval.
• Document parsing for PDF, Microsoft Word, and image-based documents.
• Content extraction, normalization, and chunking.
• Retrieval Augmented Generation (RAG) pipeline.
• Vector embedding generation and semantic retrieval.
• Natural-language Q&A across uploaded documents.
• Long-document summarization.
• Relevant-passage retrieval and cross-document knowledge discovery.
• Multi-model integration with OpenAI, Gemini, and Claude.
• Task-specific model routing and provider fallback.
• Streaming AI responses.
• Token usage tracking and AI cost monitoring.
• Firebase Authentication and secure document storage.
• User- and document-level access controls.
• API usage and error monitoring.

Benefits

• Faster Document Discovery: Semantic search helps users find relevant information based on meaning rather than exact keyword matches.
• Simplified Document Understanding: Summarization and natural-language Q&A make long documents easier to explore.
• More Focused AI Responses: RAG retrieves relevant passages to provide the selected model with focused document context.
• Flexible AI Provider Selection: Multi-model orchestration supports routing based on task requirements, context length, cost, and output quality.
• Improved Provider Resilience: Fallback handling helps maintain functionality when an AI provider is unavailable or unsuitable.
• Greater Cost Visibility: Token tracking and cost monitoring support oversight of AI usage across providers.
• More Responsive Interactions: Streaming responses let users see generated answers incrementally.
• Controlled Document Access: Firebase security rules establish user- and document-level access boundaries.

My Role

As the AI Full Stack Developer, I owned frontend and backend development, AI architecture, document intelligence workflows, security integration, and production delivery.

I built the Next.js application, integrated OpenAI, Gemini, and Claude, developed the RAG pipeline, implemented semantic search and document Q&A, integrated Firebase services, established access controls, and set up streaming responses, CI/CD, and production monitoring.

Core Expertise Demonstrated

AI full-stack development, AI SaaS, Next.js, React, TypeScript, Tailwind CSS, OpenAI API, Google Gemini, Anthropic Claude, multi-model AI orchestration, LLM integration, Retrieval Augmented Generation (RAG), vector embeddings, semantic search, document parsing, text summarization, natural-language Q&A, Firebase Authentication, Firestore, Firebase Functions, Cloud Storage, security rules, streaming responses, serverless architecture, CI/CD, and API monitoring.