Transform your documents into intelligent conversations. Upload PDFs, Word documents, images, and more. Chat with AI, extract insights, and analyze content with advanced semantic search.
A startup from Fresno, the United States, founded by Chris Nzouat.
This page is designed to help you find out whether Document-Chat-system.vercel.app is good and if it is the right choice for you.
I recently Open-Sourced Document Chat โ a completely free, open-source platform that lets you upload documents and have intelligent AI conversations with them. Built with Next.js 15, powered by multiple AI providers, and ready to deploy in minutes.
๐ Test it out: https://document-chat-system.vercel.app
๐ป GitHub: https://github.com/watat83/document-chat-system
๐ฅ Watch Video Explainer: https://youtu.be/P42nlCmicVM?si=maIjXVxaKWkvevn9
The Problem Weโre drowning in documents. PDFs, Word files, research papers, contracts, manuals, reports โ they pile up faster than we can read them. And when we need specific information? We spend hours searching, skimming, and hoping we havenโt missed something important.
AI assistants like ChatGPT have shown us a better way โ natural language conversations. But thereโs a catch: they donโt know about YOUR documents. Sure, you can copy-paste snippets, but thatโs manual, tedious, and limited by context windows.
How to Contribute โญ Star the repo โ It helps others discover the project
๐ Report bugs โ Open an issue on GitHub
๐ก Suggest features โ Share your ideas
๐ง Submit PRs โ Code contributions welcome
๐ Improve docs โ Help others get started
๐ฌ Join discussions โ Share use cases and feedback: https://discord.gg/ubWcC2PS
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AI-Powered Document Chat
Upload documents (PDF, DOCX, TXT, images) and have intelligent conversations about their content Uses RAG (Retrieval Augmented Generation) to provide accurate, context-aware responses Supports multiple AI providers: OpenRouter (100+ models), OpenAI, Anthropic Real-time streaming responses for better user experience
Semantic Document Search
Vector-based semantic search using Pinecone or pgvector Find relevant information even when exact keywords don't match Hybrid search combining vector similarity and keyword matching Document chunking for optimal context retrieval
Advanced Document Processing
Background processing pipeline using Inngest for reliability Automatic text extraction from multiple file formats Document sectioning and analysis AI-powered content summarization and metadata extraction OCR support for image-based documents
Intelligent Folder Management
Hierarchical folder organization with drag-and-drop Color-coded folders for visual organization Bulk operations (move, delete, archive) Smart folder navigation and breadcrumbs
Usage Tracking & Billing
Real-time usage metrics (documents, storage, AI requests) Stripe integration for subscription management Multiple pricing tiers (Starter, Professional, Agency) Usage limit enforcement with grace periods
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Cost Control & Transparency: Our BYOK feature and multi-provider support mean you're never locked into expensive proprietary pricing. Use your own keys or let us handle it - your choice.
Production-Ready Architecture: Built with enterprise patterns like circuit breakers, automatic retries, audit logging, and multi-tenant isolation from day one. Not a prototype - it's ready for real workloads.
True Open Source: MIT licensed with no vendor lock-in. Self-host it, fork it, customize it - you own your data and your deployment.
Developer-First Design: Clean API architecture, comprehensive TypeScript types, modular design. Easy to extend and integrate into existing workflows.
Reliability: Background job processing with Inngest ensures document processing never fails silently. Automatic retries, error tracking, and graceful degradation built-in.
Primary Audience: Small to medium development teams and tech-savvy businesses who need intelligent document management but want to avoid vendor lock-in and maintain control over their AI costs.
Secondary Audience: Individual developers and consultants building custom solutions for clients who need RAG (Retrieval Augmented Generation) capabilities without starting from scratch.
Tertiary Audience: Enterprises evaluating open-source alternatives to expensive proprietary document AI platforms, especially those with compliance requirements around data sovereignty.
Common Traits: - Technical literacy - Cost-conscious - Value transparency - Prefer self-hosted or hybrid deployment models - Need customization capabilities
Document Chat System emerged from frustration with existing document AI platforms that force you into expensive proprietary ecosystems. As developers building RAG applications, we found ourselves repeatedly implementing the same patterns: vector search, document chunking, multi-provider AI routing, background processing. Every project started from scratch or relied on closed-source platforms charging premium prices.
We decided to build what we wished existed: a production-ready, open-source foundation for document AI that handles all the complex infrastructure - vector databases, background jobs, multi-tenancy, cost optimization - while remaining completely transparent and customizable.
The goal wasn't to create another SaaS platform but to provide a solid starting point for anyone building document intelligence features, whether for their own products or client projects. By open-sourcing it under MIT license, we ensure the community benefits from enterprise-grade patterns without enterprise pricing.
Document Chat System stands out through its multi-provider AI architecture that gives users unprecedented flexibility and cost control. Unlike competitors locked into a single AI provider, we support OpenRouter (100+ models), OpenAI, and Anthropic with intelligent routing that automatically selects the best model for each task.
Our BYOK (Bring Your Own Key) feature lets users provide their own API keys for complete cost transparency and privacy. We combine this with a production-ready background processing pipeline using Inngest for reliable document processing, and dual vector search support (Pinecone/pgvector) giving users choice between managed cloud or self-hosted solutions.
As an open-source project launched in October 2025, Document Chat System is in early adoption phase. Current usage is primarily:
Note: As an MIT-licensed open-source project, we don't track "customers" in the traditional sense. Success is measured by community adoption, contributions, and production deployments rather than enterprise contracts.
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