AuditMind: Multi-Agent Document Intelligence, From Days to Minutes

This project involved architecting and building an end-to-end document intelligence platform for a construction technology client processing large volumes of compliance and audit documents.

The system needed to handle multi-hundred-page PDFs, extract structured information across multiple document types simultaneously, and surface findings in near real-time to downstream services. Speed, accuracy, and production-grade reliability were all non-negotiable requirements.

We designed a multi-agent orchestration architecture using LangGraph for agent coordination, Celery for distributed task execution, and Redis as the message broker and result cache.

Key architectural decisions:

• **LangGraph orchestration layer**: A supervisor agent fans out to specialized sub-agents (clause extractor, compliance checker, risk flagging agent) running in parallel across Celery workers
• **AWS S3 streaming**: Documents are chunked and streamed directly from S3 into vector DB embeddings without full local downloads, reducing latency by ~60%
• **Vector DB indexing**: Chunks are embedded and stored in a vector database for semantic retrieval by each specialized agent, enabling precise, context-aware extraction
• **Server-Sent Events (SSE)**: Results stream to the client frontend in real-time as agents complete their work, rather than waiting for the full batch
• **Retry and dead-letter queues**: Celery task routing ensures failed documents are retried with backoff and surfaced to an admin queue for manual review
• **Async FastAPI backend**: The API layer handles thousands of concurrent SSE connections without thread-per-connection overhead