Enterprise & Management · Artificial Intelligence
Enterprise Support AI Hub
Company Knowledge Assistant
Answers support questions from your own documents, with sources. Production-grade RAG platform that turns a company's documents, images and audio into a source-grounded support and knowledge assistant, with cost analytics and step-level observability.
Overview
What this project does
Centralizes corporate knowledge across multiple document formats, processes it through advanced LLM systems, and delivers accurate, source-grounded answers for automated customer support, internal knowledge queries, and business strategy. Functions as an "AI brain" for enterprises to leverage their documentation without hallucinations.
Features
Core features
- Multimodal knowledge base ingestion (PDF, DOCX, CSV, JSON, Markdown, HTML, PPTX)
- OCR for image processing and transcription for audio/video (.mp3, .wav, .mp4)
- RAG chat system grounded in company documents with hallucination prevention
- Conversational memory with sliding 10-message window for token optimization
- Toggleable DuckDuckGo web-search fallback with live page crawling
- Cost & analytics dashboard (token usage, latency, active users, API spend)
- Observability inspector with step tracing and RAG similarity metrics
- Dark mode and responsive UI design
- Serverless upload bypass for files over 4MB via local-disk indexing
Impact
What it delivers
- Production-grade RAG system with dual-database architecture
- Document processing across 8+ formats with advanced media handling
- Multi-LLM support with cost-conscious memory optimization
- Conversational AI with full audit trails and observability
- Enterprise-scale knowledge centralization and retrieval
- Cost analytics and ROI transparency
- Hallucination-free responses through source grounding
- Serverless scalability for large file handling
- Dual-database architecture combining MongoDB and ChromaDB for optimal performance
- Multimodal processing with OCR and audio/video transcription
- Sliding window memory optimization for cost reduction
- Step-level tracing and RAG similarity inspection for debugging
- Dark mode with micro-animations
- Real-time cost and token usage analytics
Under the hood
Technical specifications
- Type
- Production-grade RAG (Retrieval-Augmented Generation) platform and enterprise AI workspace for automated customer support and knowledge management.
- Target users
- Enterprise teams, customer support managers, knowledge workers, CTOs, and business decision-makers seeking automated customer support solutions, knowledge base automation, and AI-powered documentation systems with cost transparency and observability.
- Admin & management
- Platform includes knowledge base management controls, document ingestion workflows, analytics and cost tracking interface, observability inspection tools, and audit trace logging for enterprise compliance.
- Integrations
- OpenAI API (GPT-4o, GPT-4o-mini) Google Gemini API (1.5 Pro, Flash) LangChain framework integration DuckDuckGo web search API Vercel deployment and serverless infrastructure
- Frontend
- Next.js 16 (App Router, Turbopack), TypeScript, React, Tailwind CSS
- Database
- MongoDB via Mongoose (metadata, sessions, audit traces)
- Vector Store
- ChromaDB (high-dimensional embeddings, semantic search)
- AI/LLM
- LangChain, OpenAI (GPT-4o, GPT-4o-mini), Google Gemini (1.5 Pro, Flash)
- Embeddings
- text-embedding-ada-002
- Additional
- Zod validation, DuckDuckGo search integration
- Deployment
- Vercel for performance and scalability
Related work
