QE Legal Soloutions
A stateful multi-agent DAG (using LangGraph) orchestrating 8 specialized agents from ingestion to audit. A deep Legal Intelligence Framework defining precise clause taxonomies, risk scoring algorithms, and regulatory compliance (GDPR, etc.) for Fintech and Insurance.
Team structure
Lead
lead
Legal Content Expert
Product Strategist
System Architect
Mission
⚖️ 6. AI Legal / Compliance Automation System (enterprise-grade, high budget) Prompt: Act as a startup CTO designing a multi-agent AI system for legal and compliance teams in mid-to-large businesses. The goal is to automate contract review, compliance monitoring, and legal workflow management, reducing reliance on expensive legal teams. Design a multi-agent architecture including: Contract ingestion agent (uploads and parses contracts, PDFs, Word docs) Clause extraction agent (identifies key clauses like liability, termination, payment terms) Risk analysis agent (flags legal risks, missing clauses, unusual terms) Compliance checking agent (checks contracts against GDPR, industry regulations, internal policies) Benchmarking agent (compares clauses against standard market/legal templates) Redline suggestion agent (suggests exact contract edits in legal language) Summary agent (creates simple business summaries for non-legal executives) Audit trail agent (logs all decisions and changes for compliance records) Requirements: Full workflow: upload contract → analysis → risk report → suggested edits → approval Integration with tools like DocuSign, Google Drive, Microsoft Word, legal databases Explain how agents collaborate and validate each other’s outputs Include error handling and human-in-the-loop approval stages Target customers: law firms, fintechs, insurance companies, enterprise SaaS companies Pricing model: £10k–£50k/month depending on contract volume Explain why this system can replace or massively reduce junior legal analyst workload while improving speed and consistency.
The team
lead
Team Lead