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.