Implement Custom AI for Due Diligence Document Review

Many small law firms rely on the keyword search function within their Document Management System (DMS) like NetDocuments. This approach can find specific phrases but fails to understand context. Searching for 'indemnification' misses clauses phrased as 'hold harmless,' forcing attorneys into a time-consuming manual review of every variation. These systems cannot compare a clause's substance against a firm-approved standard.

Consider a junior associate tasked with reviewing a 500-page data room for an M&A deal. They work with a 20-page checklist in a Word document on a second monitor. For each contract, they manually search for key terms, copy relevant clauses into a report, and visually compare them against the firm's standard language. This process takes 8-10 hours, is prone to human error from fatigue, and uses expensive billable hours on a repetitive, low-value task.

Larger, off-the-shelf AI review platforms exist, but they are built for enterprise scale and pricing. They often present a black-box model, making it impossible to understand why a clause was flagged. Furthermore, their pre-trained models may not align with a small firm's niche practice area or specific risk tolerance, and customizing them is either impossible or prohibitively expensive. You cannot easily integrate your firm's own curated clause library, the very intellectual property that gives you a competitive edge.

The structural issue is that generic tools are not built for a firm's unique intellectual capital. A DMS is a digital file cabinet, and enterprise AI is a one-size-fits-all solution. A 10-attorney firm needs a system that embeds its own legal expertise and review logic directly into the workflow, which these platforms are not designed to do.

The engagement would begin with an audit of your current review process and documents. Syntora would analyze 5-10 anonymized examples of the target contracts and your firm's standard clause library. This initial phase maps out the key clauses, identifies acceptable variations, and defines the logic for flagging non-standard terms. You receive a technical specification outlining the extraction rules before any code is written.

The technical approach would use a FastAPI backend service to manage the document processing pipeline. When a PDF is uploaded to a designated AWS S3 bucket, a function triggers. The system performs OCR if needed, then sends the text to the Claude API with a detailed prompt to identify and extract specific clauses. These extractions are then compared against your firm's pre-approved language stored in a Supabase database. This architecture is efficient, processing a 50-page contract in under 90 seconds.

The final deliverable is a simple web application where attorneys can upload documents and view the results. The interface would present a summary of flagged clauses, showing the contract's language side-by-side with your firm's standard, with differences highlighted. This creates a human-in-the-loop gate, allowing attorneys to make the final judgment call with all the information organized for them. The system also generates an audit trail for every document reviewed.