Build Custom AI for Legal Research and Due Diligence

Small firms often rely on practice management software like Clio or MyCase for document storage. These systems offer basic search but lack semantic understanding. A search for "indemnification clause" will only find documents with that exact phrase, missing variations like "hold harmless" and providing no context on whether the clause is standard or high-risk. This leaves associates spending hours manually scanning PDFs.

Consider a 15-attorney firm handling commercial real estate transactions. During due diligence for a property acquisition, an associate must review 50 lease agreements. Using standard tools, they open each PDF, search for keywords like "termination," "sublet," and "default," then copy-paste relevant text into a summary memo. This process takes over 20 hours of non-billable time and is prone to human error; missing one non-standard renewal option could cost the client millions.

The structural problem is that tools like LexisNexis or Westlaw offer powerful research databases but are closed ecosystems. They cannot be trained on a firm's private document history or integrated with internal workflows. You cannot feed them your firm's specific clause library to flag deviations in a new contract. The architectural limitation is that these platforms are designed for searching public data, not analyzing a firm's private, client-specific documents in a secure, automated workflow.

An engagement would begin with an audit of your firm's existing documents and workflows. Syntora would map the entire lifecycle of a key document, from intake to final analysis. For a contract review system, this means identifying your standard clauses, common deviations, and the specific risk factors your attorneys look for. This discovery phase produces a detailed architectural plan.

The core of the system would be a FastAPI service using the Claude API for document parsing and analysis. FastAPI is chosen for its performance and built-in data validation, ensuring data integrity. Documents would be stored in AWS S3, and analysis results stored in a Supabase database. This architecture provides an auditable trail and allows data to remain within your firm's control. We've built similar document processing pipelines for financial services, and the same pattern applies to legal contracts.

The final deliverable is a secure, internal web application. Attorneys could upload a contract and receive an annotated version in under 60 seconds, with non-standard clauses flagged and compared against your firm's approved library. Every automated decision would pass through a human-in-the-loop interface, allowing an attorney to approve or reject the AI's findings before they are finalized.