LEXIS AI - Legal Intelligence Platform

The client's legal department was drowning in contract volume. Senior attorneys billing at $600 per hour were spending an average of four hours per contract on initial review — reading through indemnification clauses, liability caps, change-of-control provisions, and cross-references to governing law. With 1,200+ contracts flowing through the pipeline each quarter, the team was burning through $2.8 million annually on first-pass reviews alone, and the backlog was growing by 15% quarter over quarter.

The problem was compounded by inconsistency. Different attorneys flagged different risks depending on their specialization and experience level. Junior associates missed subtle carve-outs in limitation-of-liability clauses roughly 23% of the time, and cross-referencing against the firm's 50,000+ precedent library was practically impossible within the time constraints of a standard review cycle. The firm had tried rules-based contract analysis tools, but they broke down on non-standard clause structures and couldn't handle the nuance of context-dependent risk assessment.

Beyond pure cost, the bottleneck was creating strategic risk. Deals were delayed by days waiting for legal review. The general counsel needed a system that could perform clause-level risk analysis at machine speed while maintaining the accuracy standard required for Fortune 500 transactions — and critically, every citation and risk flag had to be traceable back to a specific precedent or regulatory source. Zero tolerance for hallucinated legal references.

The platform runs on a three-tier architecture designed for low-latency inference and auditability. The ingestion tier handles document processing: PDFs and Word documents are parsed using a custom document processor that preserves structural hierarchy (articles, sections, clauses, sub-clauses). A fine-tuned legal NER model segments documents into atomic clauses and extracts defined terms, cross-references, and party identifiers. Processed clauses are embedded using our domain-adapted embedding model and stored in PostgreSQL with pgvector, alongside full metadata in a relational schema.

The retrieval and reasoning tier is the core intelligence layer. It consists of a FastAPI service that orchestrates the multi-stage retrieval pipeline: query expansion, hybrid search (dense + sparse), cross-encoder re-ranking, and context assembly. The reasoning engine uses Claude and GPT-4 in a dual-model architecture — one model generates initial analysis, the other performs adversarial verification. Both models operate within strict prompt constraints that enforce citation requirements and prevent confabulation. The entire reasoning chain is logged for auditability.

The presentation tier serves a React frontend with real-time streaming of analysis results via WebSocket. Attorneys see a split-pane interface: the original contract on the left with highlighted risk clauses, and the AI analysis on the right with expandable precedent references. The system includes an annotation layer where attorneys can accept, modify, or reject AI recommendations, creating a feedback loop that continuously improves model performance. Infrastructure runs on AWS with ECS for container orchestration, ElastiCache for caching frequently-accessed precedents, and CloudWatch for monitoring pipeline latency and accuracy metrics.