AI Agents for Legal Automation

The Bottle Neck

LegalTech Pro was drowning in paperwork. Their paralegals spent 70% of their time manually classifying scanned PDFs, extracting key dates, and renaming files. It was tedious, error-prone, and expensive.

The Agentic Solution

We built a multi-agent system using LangGraph to mimic the human workflow.

Classifier Agent: "Looks" at the document (using GPT-4 Vision) to determine if it's a Contract, Court Filing, or Invoice.
Extractor Agent: Uses schema-guided generation to extract specific fields (Plaintiff, Defendant, Settlement Amount) based on the document type.
Auditor Agent: Double-checks the extracted data against valid entity lists and business rules.

Architecture

State is managed via a graph where each node is a specialized LLM call. If the Auditor Agent detects low confidence, the workflow routes to a "Human Review" node, creating a task in the staff dashboard.

Key Innovations

Self-Correction: If the JSON output is malformed, the agent "sees" the error and retries with a correction prompt.
Multimodal: Handles scanned images and native PDFs seamlessly.

Tech Specs

Orchestration: LangGraph, LangChain
Models: GPT-4o, Claude 3.5 Sonnet (for complex legal reasoning)
Storage: MongoDB, AWS S3
Frontend: Shadcn UI + React Flow for visualizing the agent decisions

Impact

10,000+ documents processed daily.
95% accuracy rate (comparable to human junior paralegals).
4 FTEs redeployed to high-value case research instead of data entry.