How I Built an AI-Powered Document Intelligence Platform Using Next.js, FastAPI, RAG and MCP

说明与用途

该平台自动化处理、提取、验证和人工审批业务文档，降低人工工作量并保持人类监督。

问题

Manual processing of invoices, reports, contracts and other business documents is time‑consuming and difficult to manage efficiently.

结果

The platform automates document intake, extraction, validation, and human approval, reducing manual effort while maintaining accountability.

工作流

• Upload PDF/JPG/PNG to backend
• Validate file type and size
• Store original in AWS S3 and metadata in PostgreSQL
• Classify document type and extract fields via LLM
• Validate extracted data against business rules
• Index structured data in ChromaDB for RAG
• Schedule review via MCP Google Calendar integration
• Manager reviews extracted info and approves or rejects
• Record approval status and audit trail

证据

Manual processing can become time-consuming and difficult to manage efficiently.

来源

The platform transforms uploaded documents into structured, searchable, and reviewable information.

来源

Upload → Classification → Information Extraction → Validation → Indexing → Human Review → Approval or Rejection

来源